Privacy-preserving restricted boltzmann machine.

PubMed

Li, Yu; Zhang, Yuan; Ji, Yue

2014-01-01

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model.

Privacy-Preserving Restricted Boltzmann Machine

PubMed Central

Li, Yu

2014-01-01

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the accuracy is very close to the original RBM model. PMID:25101139

Who Prophets from Big Data in Education? New Insights and New Challenges

ERIC Educational Resources Information Center

Lynch, Collin F.

2017-01-01

Big Data can radically transform education by enabling personalized learning, deep student modeling, and true longitudinal studies that compare changes across classrooms, regions, and years. With these promises, however, come risks to individual privacy and educational validity, along with deep policy and ethical issues. Education is largely a…

Internet-based brain training games, citizen scientists, and big data: ethical issues in unprecedented virtual territories.

PubMed

Purcell, Ryan H; Rommelfanger, Karen S

2015-04-22

Internet brain training programs, where consumers serve as both subjects and funders of the research, represent the closest engagement many individuals have with neuroscience. Safeguards are needed to protect participants' privacy and the evolving scientific enterprise of big data. Copyright © 2015 Elsevier Inc. All rights reserved.

Internet Addiction of Young Greek Adults: Psychological Aspects and Information Privacy.

PubMed

Grammenos, P; Syrengela, N A; Magkos, E; Tsohou, A

2017-01-01

The main goal of this study is to examine the Internet addiction status of Greek young adults, aged from 18 to 25, using Young's Internet Addiction Test (IAT) and self-administered questionnaires. In addition this paper assesses the psychological traits of addicted persons per addiction category, using the big five factor model tool to study the user's personality and analyze the components that lead a person to become Internet addicted. Furthermore, we found an association between addicted people and the five factors from the Big Five Factor Model; i.e., extraversion, agreeableness, conscientiousness, neuroticism, openness to experience. Moreover, this paper discusses information privacy awareness issues related to Internet Addiction treatment.

Children's Privacy in the Big Data Era: Research Opportunities.

PubMed

Montgomery, Kathryn C; Chester, Jeff; Milosevic, Tijana

2017-11-01

This article focuses on the privacy implications of advertising on social media, mobile apps, and games directed at children. Academic research on children's privacy has primarily focused on the safety risks involved in sharing personal information on the Internet, leaving market forces (such as commercial data collection) as a less discussed aspect of children's privacy. Yet, children's privacy in the digital era cannot be fully understood without examining marketing practices, especially in the context of "big data." As children increasingly consume content on an ever-expanding variety of digital devices, media and advertising industries are creating new ways to track their behaviors and target them with personalized content and marketing messages based on individual profiles. The advent of the so-called Internet of Things, with its ubiquitous sensors, is expanding these data collection and profiling practices. These trends raise serious concerns about digital dossiers that could follow young people into adulthood, affecting their access to education, employment, health care, and financial services. Although US privacy law provides some safeguards for children younger than 13 years old online, adolescents are afforded no such protections. Moreover, scholarship on children and privacy continues to lag behind the changes taking place in global media, advertising, and technology. This article proposes collaboration among researchers from a range of fields that will enable cross-disciplinary studies addressing not only the developmental issues related to different age groups but also the design of digital media platforms and the strategies used to influence young people. Copyright © 2017 by the American Academy of Pediatrics.

Privacy preserving interactive record linkage (PPIRL).

PubMed

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.

An overview of human genetic privacy

PubMed Central

Shi, Xinghua; Wu, Xintao

2016-01-01

The study of human genomics is becoming a Big Data science, owing to recent biotechnological advances leading to availability of millions of personal genome sequences, which can be combined with biometric measurements from mobile apps and fitness trackers, and of human behavior data monitored from mobile devices and social media. With increasing research opportunities for integrative genomic studies through data sharing, genetic privacy emerges as a legitimate yet challenging concern that needs to be carefully addressed, not only for individuals but also for their families. In this paper, we present potential genetic privacy risks and relevant ethics and regulations for sharing and protecting human genomics data. We also describe the techniques for protecting human genetic privacy from three broad perspectives: controlled access, differential privacy, and cryptographic solutions. PMID:27626905

Privacy, Security, & Compliance: Strange Bedfellows or a Marriage Made in Heaven?

ERIC Educational Resources Information Center

Corn, Michael; Rosenthal, Jane

2013-01-01

Where does privacy belong in the college/university ecosystem, and what should its relationship be with security and compliance? Are the three areas best kept separate and distinct? Should there be some overlap? Or would a single office, officer, and/or reporting line enable a big picture of the whole? This article examines several of the campus…

Protecting privacy in a clinical data warehouse.

PubMed

Kong, Guilan; Xiao, Zhichun

2015-06-01

Peking University has several prestigious teaching hospitals in China. To make secondary use of massive medical data for research purposes, construction of a clinical data warehouse is imperative in Peking University. However, a big concern for clinical data warehouse construction is how to protect patient privacy. In this project, we propose to use a combination of symmetric block ciphers, asymmetric ciphers, and cryptographic hashing algorithms to protect patient privacy information. The novelty of our privacy protection approach lies in message-level data encryption, the key caching system, and the cryptographic key management system. The proposed privacy protection approach is scalable to clinical data warehouse construction with any size of medical data. With the composite privacy protection approach, the clinical data warehouse can be secure enough to keep the confidential data from leaking to the outside world. © The Author(s) 2014.

Using the Personal Health Train for Automated and Privacy-Preserving Analytics on Vertically Partitioned Data.

PubMed

van Soest, Johan; Sun, Chang; Mussmann, Ole; Puts, Marco; van den Berg, Bob; Malic, Alexander; van Oppen, Claudia; Towend, David; Dekker, Andre; Dumontier, Michel

2018-01-01

Conventional data mining algorithms are unable to satisfy the current requirements on analyzing big data in some fields such as medicine, policy making, judicial, and tax records. However, applying diverse datasets from different institutes (both healthcare and non-healthcare related) can enrich information and insights. So far, analyzing this data in an automated, privacy-preserving manner does not exist to our knowledge. In this work, we propose an infrastructure, and proof-of-concept for privacy-preserving analytics on vertically partitioned data.

Big Data in the Campus Landscape: Security and Privacy. ECAR Working Group Paper

ERIC Educational Resources Information Center

Barnett, William; Corn, Mike; Hillegas, Curt; Wada, Kent

2015-01-01

This paper is part of series of the EDUCAUSE Center for Analysis and Research Campus Cyberinfrastructure (ECAR-CCI) Working Group. The topic of big data continues to receive a great deal of publicity because of its promise for opening new avenues of scholarly discovery and commercial opportunity. The ability to sift rapidly through massive amounts…

An overview of human genetic privacy.

PubMed

Shi, Xinghua; Wu, Xintao

2017-01-01

The study of human genomics is becoming a Big Data science, owing to recent biotechnological advances leading to availability of millions of personal genome sequences, which can be combined with biometric measurements from mobile apps and fitness trackers, and of human behavior data monitored from mobile devices and social media. With increasing research opportunities for integrative genomic studies through data sharing, genetic privacy emerges as a legitimate yet challenging concern that needs to be carefully addressed, not only for individuals but also for their families. In this paper, we present potential genetic privacy risks and relevant ethics and regulations for sharing and protecting human genomics data. We also describe the techniques for protecting human genetic privacy from three broad perspectives: controlled access, differential privacy, and cryptographic solutions. © 2016 New York Academy of Sciences.

Privacy preserving interactive record linkage (PPIRL)

PubMed Central

Kum, Hye-Chung; Krishnamurthy, Ashok; Machanavajjhala, Ashwin; Reiter, Michael K; Ahalt, Stanley

2014-01-01

Objective Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human–machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data. Methods In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding. Results We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework. Conclusions Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human–machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility. PMID:24201028

Big Data: Implications for Health System Pharmacy

PubMed Central

Stokes, Laura B.; Rogers, Joseph W.; Hertig, John B.; Weber, Robert J.

2016-01-01

Big Data refers to datasets that are so large and complex that traditional methods and hardware for collecting, sharing, and analyzing them are not possible. Big Data that is accurate leads to more confident decision making, improved operational efficiency, and reduced costs. The rapid growth of health care information results in Big Data around health services, treatments, and outcomes, and Big Data can be used to analyze the benefit of health system pharmacy services. The goal of this article is to provide a perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for pharmacy leaders to effectively use Big Data. A few strategies involved in managing Big Data in health system pharmacy include identifying potential opportunities for Big Data, prioritizing those opportunities, protecting privacy concerns, promoting data transparency, and communicating outcomes. As health care information expands in its content and becomes more integrated, Big Data can enhance the development of patient-centered pharmacy services. PMID:27559194

Big Data: Implications for Health System Pharmacy.

PubMed

Stokes, Laura B; Rogers, Joseph W; Hertig, John B; Weber, Robert J

2016-07-01

Big Data refers to datasets that are so large and complex that traditional methods and hardware for collecting, sharing, and analyzing them are not possible. Big Data that is accurate leads to more confident decision making, improved operational efficiency, and reduced costs. The rapid growth of health care information results in Big Data around health services, treatments, and outcomes, and Big Data can be used to analyze the benefit of health system pharmacy services. The goal of this article is to provide a perspective on how Big Data can be applied to health system pharmacy. It will define Big Data, describe the impact of Big Data on population health, review specific implications of Big Data in health system pharmacy, and describe an approach for pharmacy leaders to effectively use Big Data. A few strategies involved in managing Big Data in health system pharmacy include identifying potential opportunities for Big Data, prioritizing those opportunities, protecting privacy concerns, promoting data transparency, and communicating outcomes. As health care information expands in its content and becomes more integrated, Big Data can enhance the development of patient-centered pharmacy services.

Telecom Big Data for Urban Transport Analysis - a Case Study of Split-Dalmatia County in Croatia

NASA Astrophysics Data System (ADS)

Baučić, M.; Jajac, N.; Bućan, M.

2017-09-01

Today, big data has become widely available and the new technologies are being developed for big data storage architecture and big data analytics. An ongoing challenge is how to incorporate big data into GIS applications supporting the various domains. International Transport Forum explains how the arrival of big data and real-time data, together with new data processing algorithms lead to new insights and operational improvements of transport. Based on the telecom customer data, the Study of Tourist Movement and Traffic in Split-Dalmatia County in Croatia is carried out as a part of the "IPA Adriatic CBC//N.0086/INTERMODAL" project. This paper briefly explains the big data used in the study and the results of the study. Furthermore, this paper investigates the main considerations when using telecom customer big data: data privacy and data quality. The paper concludes with GIS visualisation and proposes the further use of big data used in the study.

[Big Data: the great opportunities and challenges to microbiome and other biomedical research].

PubMed

Xu, Zhenjiang

2015-02-01

With the development of high-throughput technologies, biomedical data has been increasing exponentially in an explosive manner. This brings enormous opportunities and challenges to biomedical researchers on how to effectively utilize big data. Big data is different from traditional data in many ways, described as 3Vs - volume, variety and velocity. From the perspective of biomedical research, here I introduced the characteristics of big data, such as its messiness, re-usage and openness. Focusing on microbiome research of meta-analysis, the author discussed the prospective principles in data collection, challenges of privacy protection in data management, and the scalable tools in data analysis with examples from real life.

Big Data in the Earth Observing System Data and Information System

NASA Technical Reports Server (NTRS)

Lynnes, Chris; Baynes, Katie; McInerney, Mark

2016-01-01

Approaches that are being pursued for the Earth Observing System Data and Information System (EOSDIS) data system to address the challenges of Big Data were presented to the NASA Big Data Task Force. Cloud prototypes are underway to tackle the volume challenge of Big Data. However, advances in computer hardware or cloud won't help (much) with variety. Rather, interoperability standards, conventions, and community engagement are the key to addressing variety.

Creating value in health care through big data: opportunities and policy implications.

PubMed

Roski, Joachim; Bo-Linn, George W; Andrews, Timothy A

2014-07-01

Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised. Project HOPE—The People-to-People Health Foundation, Inc.

[Big Data- challenges and risks].

PubMed

Krauß, Manuela; Tóth, Tamás; Hanika, Heinrich; Kozlovszky, Miklós; Dinya, Elek

2015-12-06

The term "Big Data" is commonly used to describe the growing mass of information being created recently. New conclusions can be drawn and new services can be developed by the connection, processing and analysis of these information. This affects all aspects of life, including health and medicine. The authors review the application areas of Big Data, and present examples from health and other areas. However, there are several preconditions of the effective use of the opportunities: proper infrastructure, well defined regulatory environment with particular emphasis on data protection and privacy. These issues and the current actions for solution are also presented.

Big data for health.

PubMed

Andreu-Perez, Javier; Poon, Carmen C Y; Merrifield, Robert D; Wong, Stephen T C; Yang, Guang-Zhong

2015-07-01

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Toward a Literature-Driven Definition of Big Data in Healthcare

PubMed Central

Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

2015-01-01

Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data. PMID:26137488

Toward a Literature-Driven Definition of Big Data in Healthcare.

PubMed

Baro, Emilie; Degoul, Samuel; Beuscart, Régis; Chazard, Emmanuel

2015-01-01

The aim of this study was to provide a definition of big data in healthcare. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

Acoustical and noise redesign considerations when trying to increase patient privacy while ensuring comfort

NASA Astrophysics Data System (ADS)

Klavetter, Eric

2005-09-01

An internal assessment was undertaken to understand the flow of patients to ensure comfort and privacy during their health care experience at Mayo Clinic. A number of different prototypes, work flows, and methodologies were utilized and assessed to determine the ``best experience for our patients.'' A number of prototypes ranging from self-check in to personal pagers were assessed along with creating environments that introduced ``passive distractions'' for acoustical and noise management, which can range from fireplaces, to coffee shops to playgrounds to ``tech corridors.'' While a number of these designs are currently being piloted, the over-reaching goal is to make the patient experience ``like no other'' when receiving their care at Mayo Clinic.

Big data in biomedicine.

PubMed

Costa, Fabricio F

2014-04-01

The increasing availability and growth rate of biomedical information, also known as 'big data', provides an opportunity for future personalized medicine programs that will significantly improve patient care. Recent advances in information technology (IT) applied to biomedicine are changing the landscape of privacy and personal information, with patients getting more control of their health information. Conceivably, big data analytics is already impacting health decisions and patient care; however, specific challenges need to be addressed to integrate current discoveries into medical practice. In this article, I will discuss the major breakthroughs achieved in combining omics and clinical health data in terms of their application to personalized medicine. I will also review the challenges associated with using big data in biomedicine and translational science. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Assistive Technology System that Provides Personalized Dressing Support for People Living with Dementia: Capability Study.

PubMed

Burleson, Winslow; Lozano, Cecil; Ravishankar, Vijay; Lee, Jisoo; Mahoney, Diane

2018-05-01

Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). This laboratory study evaluated the DRESS prototype's capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing-typical issues that challenge a PWD and their caregivers. A set of expected detections for correct dressing was prepared via video analysis of all participants' dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype's ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype's reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite. ©Winslow Burleson, Cecil Lozano, Vijay Ravishankar, Jisoo Lee, Diane Mahoney. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.05.2018.

Clinical Research Informatics Contributions from 2015.

PubMed

Daniel, C; Choquet, R

2016-11-10

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

HARNESSING BIG DATA FOR PRECISION MEDICINE: INFRASTRUCTURES AND APPLICATIONS.

PubMed

Yu, Kun-Hsing; Hart, Steven N; Goldfeder, Rachel; Zhang, Qiangfeng Cliff; Parker, Stephen C J; Snyder, Michael

2017-01-01

Precision medicine is a health management approach that accounts for individual differences in genetic backgrounds and environmental exposures. With the recent advancements in high-throughput omics profiling technologies, collections of large study cohorts, and the developments of data mining algorithms, big data in biomedicine is expected to provide novel insights into health and disease states, which can be translated into personalized disease prevention and treatment plans. However, petabytes of biomedical data generated by multiple measurement modalities poses a significant challenge for data analysis, integration, storage, and result interpretation. In addition, patient privacy preservation, coordination between participating medical centers and data analysis working groups, as well as discrepancies in data sharing policies remain important topics of discussion. In this workshop, we invite experts in omics integration, biobank research, and data management to share their perspectives on leveraging big data to enable precision medicine.Workshop website: http://tinyurl.com/PSB17BigData; HashTag: #PSB17BigData.

A Systematic Literature Mapping of Risk Analysis of Big Data in Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Bee Yusof Ali, Hazirah; Marziana Abdullah, Lili; Kartiwi, Mira; Nordin, Azlin; Salleh, Norsaremah; Sham Awang Abu Bakar, Normi

2018-05-01

This paper investigates previous literature that focusses on the three elements: risk assessment, big data and cloud. We use a systematic literature mapping method to search for journals and proceedings. The systematic literature mapping process is utilized to get a properly screened and focused literature. With the help of inclusion and exclusion criteria, the search of literature is further narrowed. Classification helps us in grouping the literature into categories. At the end of the mapping, gaps can be seen. The gap is where our focus should be in analysing risk of big data in cloud computing environment. Thus, a framework of how to assess the risk of security, privacy and trust associated with big data and cloud computing environment is highly needed.

Officials nationwide give a green light to automated traffic enforcement

DOT National Transportation Integrated Search

2000-03-11

There has been resistance to using cameras to automatically identify vehicles driven by motorists who run red lights and drive faster than the posted speed limits. Fairness, privacy, and "big brother" have been cited as reasons. The article examines ...

Simple Peer-to-Peer SIP Privacy

NASA Astrophysics Data System (ADS)

Koskela, Joakim; Tarkoma, Sasu

In this paper, we introduce a model for enhancing privacy in peer-to-peer communication systems. The model is based on data obfuscation, preventing intermediate nodes from tracking calls, while still utilizing the shared resources of the peer network. This increases security when moving between untrusted, limited and ad-hoc networks, when the user is forced to rely on peer-to-peer schemes. The model is evaluated using a Host Identity Protocol-based prototype on mobile devices, and is found to provide good privacy, especially when combined with a source address hiding scheme. The contribution of this paper is to present the model and results obtained from its use, including usability considerations.

What Does Big Data Mean for Wearable Sensor Systems?

PubMed Central

Lovell, N. H.; Yang, G. Z.; Horsch, A.; Lukowicz, P.; Murrugarra, L.; Marschollek, M.

2014-01-01

Summary Objectives The aim of this paper is to discuss how recent developments in the field of big data may potentially impact the future use of wearable sensor systems in healthcare. Methods The article draws on the scientific literature to support the opinions presented by the IMIA Wearable Sensors in Healthcare Working Group. Results The following is discussed: the potential for wearable sensors to generate big data; how complementary technologies, such as a smartphone, will augment the concept of a wearable sensor and alter the nature of the monitoring data created; how standards would enable sharing of data and advance scientific progress. Importantly, attention is drawn to statistical inference problems for which big datasets provide little assistance, or may hinder the identification of a useful solution. Finally, a discussion is presented on risks to privacy and possible negative consequences arising from intensive wearable sensor monitoring. Conclusions Wearable sensors systems have the potential to generate datasets which are currently beyond our capabilities to easily organize and interpret. In order to successfully utilize wearable sensor data to infer wellbeing, and enable proactive health management, standards and ontologies must be developed which allow for data to be shared between research groups and between commercial systems, promoting the integration of these data into health information systems. However, policy and regulation will be required to ensure that the detailed nature of wearable sensor data is not misused to invade privacies or prejudice against individuals. PMID:25123733

"Big data" versus "big brother": on the appropriate use of large-scale data collections in pediatrics.

PubMed

Currie, Janet

2013-04-01

Discussions of "big data" in medicine often revolve around gene sequencing and biosamples. It is perhaps less recognized that administrative data in the form of vital records, hospital discharge abstracts, insurance claims, and other routinely collected data also offer the potential for using information from hundreds of thousands, if not millions, of people to answer important questions. However, the increasing ease with which such data may be used and reused has increased concerns about privacy and informed consent. Addressing these concerns without creating insurmountable barriers to the use of such data for research is essential if we are to avoid a "missed opportunity" in pediatrics research.

Clinic exam room design: present and future.

PubMed

Freihoefer, Kara; Nyberg, Gary; Vickery, Christine

2013-01-01

This article aims to deconstruct various design qualities and strategies of clinic exam rooms, and discuss how they influence users' interaction and behavior in the space. Relevant literature supports the advantages and disadvantages of different design strategies. Annotated exam room prototypes illustrate the design qualities and strategies discussed. Advancements in technology and medicine, along with new legislative policies, are influencing the way care providers deliver care and ultimately clinic exam room designs. The patient-centered medical home model has encouraged primary care providers to make patients more active leaders of their health plan which will influence the overall functionality and configuration of clinic exam rooms. Specific design qualities discussed include overall size, location of doors and privacy curtains, positioning of exam tables, influence of technology in the consultation area, types of seating, and placement of sink and hand sanitizing dispensers. In addition, future trends of exam room prototypes are presented. There is a general lack of published evidence to support design professionals' design solutions for outpatient exam rooms. Future research should investigate such topics as the location of exam tables and privacy curtains as they relate to patient privacy; typical size and location of consultation table as it relates to patient connection and communication; and placement of sinks and sanitization dispensers as they relate to frequency and patterns of usage. Literature review, outpatient, technology, visual privacy.

Cincinnati Big Area Additive Manufacturing (BAAM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duty, Chad E.; Love, Lonnie J.

Oak Ridge National Laboratory (ORNL) worked with Cincinnati Incorporated (CI) to demonstrate Big Area Additive Manufacturing which increases the speed of the additive manufacturing (AM) process by over 1000X, increases the size of parts by over 10X and shows a cost reduction of over 100X. ORNL worked with CI to transition the Big Area Additive Manufacturing (BAAM) technology from a proof-of-principle (TRL 2-3) demonstration to a prototype product stage (TRL 7-8).

Potentiality of Big Data in the Medical Sector: Focus on How to Reshape the Healthcare System

PubMed Central

Jee, Kyoungyoung

2013-01-01

Objectives The main purpose of this study was to explore whether the use of big data can effectively reduce healthcare concerns, such as the selection of appropriate treatment paths, improvement of healthcare systems, and so on. Methods By providing an overview of the current state of big data applications in the healthcare environment, this study has explored the current challenges that governments and healthcare stakeholders are facing as well as the opportunities presented by big data. Results Insightful consideration of the current state of big data applications could help follower countries or healthcare stakeholders in their plans for deploying big data to resolve healthcare issues. The advantage for such follower countries and healthcare stakeholders is that they can possibly leapfrog the leaders' big data applications by conducting a careful analysis of the leaders' successes and failures and exploiting the expected future opportunities in mobile services. Conclusions First, all big data projects undertaken by leading countries' governments and healthcare industries have similar general common goals. Second, for medical data that cuts across departmental boundaries, a top-down approach is needed to effectively manage and integrate big data. Third, real-time analysis of in-motion big data should be carried out, while protecting privacy and security. PMID:23882412

Potentiality of big data in the medical sector: focus on how to reshape the healthcare system.

PubMed

Jee, Kyoungyoung; Kim, Gang-Hoon

2013-06-01

The main purpose of this study was to explore whether the use of big data can effectively reduce healthcare concerns, such as the selection of appropriate treatment paths, improvement of healthcare systems, and so on. By providing an overview of the current state of big data applications in the healthcare environment, this study has explored the current challenges that governments and healthcare stakeholders are facing as well as the opportunities presented by big data. Insightful consideration of the current state of big data applications could help follower countries or healthcare stakeholders in their plans for deploying big data to resolve healthcare issues. The advantage for such follower countries and healthcare stakeholders is that they can possibly leapfrog the leaders' big data applications by conducting a careful analysis of the leaders' successes and failures and exploiting the expected future opportunities in mobile services. First, all big data projects undertaken by leading countries' governments and healthcare industries have similar general common goals. Second, for medical data that cuts across departmental boundaries, a top-down approach is needed to effectively manage and integrate big data. Third, real-time analysis of in-motion big data should be carried out, while protecting privacy and security.

Automated Decision-Making and Big Data: Concerns for People With Mental Illness.

PubMed

Monteith, Scott; Glenn, Tasha

2016-12-01

Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.

Computer Technology and Social Issues.

ERIC Educational Resources Information Center

Garson, G. David

Computing involves social issues and political choices. Issues such as privacy, computer crime, gender inequity, disemployment, and electronic democracy versus "Big Brother" are addressed in the context of efforts to develop a national public policy for information technology. A broad range of research and case studies are examined in an…

Privacy and Access Control for IHE-Based Systems

NASA Astrophysics Data System (ADS)

Katt, Basel; Breu, Ruth; Hafner, Micahel; Schabetsberger, Thomas; Mair, Richard; Wozak, Florian

Electronic Health Record (EHR) is the heart element of any e-health system, which aims at improving the quality and efficiency of healthcare through the use of information and communication technologies. The sensitivity of the data contained in the health record poses a great challenge to security. In this paper we propose a security architecture for EHR systems that are conform with IHE profiles. In this architecture we are tackling the problems of access control and privacy. Furthermore, a prototypical implementation of the proposed model is presented.

Medical privacy protection based on granular computing.

PubMed

Wang, Da-Wei; Liau, Churn-Jung; Hsu, Tsan-Sheng

2004-10-01

Based on granular computing methodology, we propose two criteria to quantitatively measure privacy invasion. The total cost criterion measures the effort needed for a data recipient to find private information. The average benefit criterion measures the benefit a data recipient obtains when he received the released data. These two criteria remedy the inadequacy of the deterministic privacy formulation proposed in Proceedings of Asia Pacific Medical Informatics Conference, 2000; Int J Med Inform 2003;71:17-23. Granular computing methodology provides a unified framework for these quantitative measurements and previous bin size and logical approaches. These two new criteria are implemented in a prototype system Cellsecu 2.0. Preliminary system performance evaluation is conducted and reviewed.

[Big data in official statistics].

PubMed

Zwick, Markus

2015-08-01

The concept of "big data" stands to change the face of official statistics over the coming years, having an impact on almost all aspects of data production. The tasks of future statisticians will not necessarily be to produce new data, but rather to identify and make use of existing data to adequately describe social and economic phenomena. Until big data can be used correctly in official statistics, a lot of questions need to be answered and problems solved: the quality of data, data protection, privacy, and the sustainable availability are some of the more pressing issues to be addressed. The essential skills of official statisticians will undoubtedly change, and this implies a number of challenges to be faced by statistical education systems, in universities, and inside the statistical offices. The national statistical offices of the European Union have concluded a concrete strategy for exploring the possibilities of big data for official statistics, by means of the Big Data Roadmap and Action Plan 1.0. This is an important first step and will have a significant influence on implementing the concept of big data inside the statistical offices of Germany.

Considerations on Geospatial Big Data

NASA Astrophysics Data System (ADS)

LIU, Zhen; GUO, Huadong; WANG, Changlin

2016-11-01

Geospatial data, as a significant portion of big data, has recently gained the full attention of researchers. However, few researchers focus on the evolution of geospatial data and its scientific research methodologies. When entering into the big data era, fully understanding the changing research paradigm associated with geospatial data will definitely benefit future research on big data. In this paper, we look deep into these issues by examining the components and features of geospatial big data, reviewing relevant scientific research methodologies, and examining the evolving pattern of geospatial data in the scope of the four ‘science paradigms’. This paper proposes that geospatial big data has significantly shifted the scientific research methodology from ‘hypothesis to data’ to ‘data to questions’ and it is important to explore the generality of growing geospatial data ‘from bottom to top’. Particularly, four research areas that mostly reflect data-driven geospatial research are proposed: spatial correlation, spatial analytics, spatial visualization, and scientific knowledge discovery. It is also pointed out that privacy and quality issues of geospatial data may require more attention in the future. Also, some challenges and thoughts are raised for future discussion.

A Framework for Privacy-preserving Classification of Next-generation PHR data.

PubMed

Koufi, Vassiliki; Malamateniou, Flora; Prentza, Andriana; Vassilacopoulos, George

2014-01-01

Personal Health Records (PHRs), integrated with data from various sources, such as social care data, Electronic Health Record data and genetic information, are envisaged as having a pivotal role in transforming healthcare. These data, lumped under the term 'big data', are usually complex, noisy, heterogeneous, longitudinal and voluminous thus prohibiting their meaningful use by clinicians. Deriving value from these data requires the utilization of innovative data analysis techniques, which, however, may be hindered due to potential security and privacy breaches that may arise from improper release of personal health information. This paper presents a HIPAA-compliant machine learning framework that enables privacy-preserving classification of next-generation PHR data. The predictive models acquired can act as supporting tools to clinical practice by enabling more effective prevention, diagnosis and treatment of new incidents. The proposed framework has a huge potential for complementing medical staff expertise as it outperforms the manual inspection of PHR data while protecting patient privacy.

Challenges and potential solutions for big data implementations in developing countries.

PubMed

Luna, D; Mayan, J C; García, M J; Almerares, A A; Househ, M

2014-08-15

The volume of data, the velocity with which they are generated, and their variety and lack of structure hinder their use. This creates the need to change the way information is captured, stored, processed, and analyzed, leading to the paradigm shift called Big Data. To describe the challenges and possible solutions for developing countries when implementing Big Data projects in the health sector. A non-systematic review of the literature was performed in PubMed and Google Scholar. The following keywords were used: "big data", "developing countries", "data mining", "health information systems", and "computing methodologies". A thematic review of selected articles was performed. There are challenges when implementing any Big Data program including exponential growth of data, special infrastructure needs, need for a trained workforce, need to agree on interoperability standards, privacy and security issues, and the need to include people, processes, and policies to ensure their adoption. Developing countries have particular characteristics that hinder further development of these projects. The advent of Big Data promises great opportunities for the healthcare field. In this article, we attempt to describe the challenges developing countries would face and enumerate the options to be used to achieve successful implementations of Big Data programs.

Replicability and 40-Year Predictive Power of Childhood ARC Types

PubMed Central

Chapman, Benjamin P.; Goldberg, Lewis R.

2011-01-01

We examined three questions surrounding the Undercontrolled, Overcontrolled, and Resilient--or Asendorpf-Robins-Caspi (ARC)--personality types originally identified by Block (1971). In analyses of the teacher personality assessments of over 2,000 children in 1st through 6th grade in 1959-1967, and follow-up data on general and cardiovascular health outcomes in over 1,100 adults recontacted 40 years later, we found: (1) Bootstrapped internal replication clustering suggested that Big Five scores were best characterized by a tripartite cluster structure corresponding to the ARC types; (2) this cluster structure was fuzzy, rather than discrete, indicating that ARC constructs are best represented as gradients of similarity to three prototype Big Five profiles; and (3) ARC types and degrees of ARC prototypicality showed associations with multiple health outcomes 40 years later. ARC constructs were more parsimonious, but neither better nor more consistent predictors than the dimensional Big Five traits. Forty-year incident cases of heart disease could be correctly identified with 68% accuracy by personality information alone, a figure approaching the 12-year accuracy of a leading medical cardiovascular risk model. Findings support the theoretical validity of ARC constructs, their treatment as continua of prototypicality rather than discrete categories, and the need for further understanding the robust predictive power of childhood personality traits for mid-life health. PMID:21744975

Exploring mobile health in a private online social network.

PubMed

Memon, Qurban A; Mustafa, Asma Fayes

2015-01-01

Health information is very vulnerable. Certain individuals or corporate organisations will continue to steal it similar to bank account data once data is on wireless channels. Once health information is part of a social network, corresponding privacy issues also surface. Insufficiently trained employees at hospitals that pay less attention to creating a privacy-aware culture will suffer loss when mobile devices containing health information are lost, stolen or sniffed. In this work, a social network system is explored as a m-health system from a privacy perspective. A model is developed within a framework of data-driven privacy and implemented on Android operating system. In order to check feasibility of the proposed model, a prototype application is developed on Facebook for different services, including: i) sharing user location; ii) showing nearby friends; iii) calculating and sharing distance moved, and calories burned; iv) calculating, tracking and sharing user heart rate; etc.

An Assistive Technology System that Provides Personalized Dressing Support for People Living with Dementia: Capability Study

PubMed Central

Lozano, Cecil; Ravishankar, Vijay; Lee, Jisoo; Mahoney, Diane

2018-01-01

Background Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. Objective In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). Methods This laboratory study evaluated the DRESS prototype’s capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing—typical issues that challenge a PWD and their caregivers. Results A set of expected detections for correct dressing was prepared via video analysis of all participants’ dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype’s ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype’s reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. Conclusions This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite. PMID:29716885

Taming Big Data: Using App Technology to Study Organizational Behavior on Social Media

ERIC Educational Resources Information Center

Bail, Christopher A.

2017-01-01

Social media websites such as Facebook and Twitter provide an unprecedented amount of qualitative data about organizations and collective behavior. Yet these new data sources lack critical information about the broader social context of collective behavior--or protect it behind strict privacy barriers. In this article, I introduce social media…

Is Big Brother Watching the Wired Campus? Information Technologies Undermine the Climate of Freedom in Academia.

ERIC Educational Resources Information Center

Earls, Alan R.

2000-01-01

Explores privacy issues raised by information technology at colleges and universities. Drawing on accounts and opinions of faculty and staff members, provides examples of current practices and policies on Internet and e-mail use and discusses the possible need for more developed policies. (EV)

Data Entry: Towards the Critical Study of Digital Data and Education

ERIC Educational Resources Information Center

Selwyn, Neil

2015-01-01

The generation and processing of data through digital technologies is an integral element of contemporary society, as reflected in recent debates over online data privacy, "Big Data" and the rise of data mining and analytics in business, science and government. This paper outlines the significance of digital data within education,…

Privacy and the Prospect Researcher.

ERIC Educational Resources Information Center

McNamee, Mike

1990-01-01

Information--who your prospects are, what they're interested in, who can best reach them, and what they're capable of giving--is the key to big gifts to institutions of higher education. Prospect research means digging into the personal and financial backgrounds of your donors. Professionals offer advice for drawing up ethical research and privacy…

Power to the People: Data Citizens in the Age of Precision Medicine

PubMed Central

Evans, Barbara J.

2017-01-01

Twentieth-century bioethics celebrated individual autonomy but framed autonomy largely in terms of an individual’s power to make decisions and act alone. The most pressing challenges of big data science in the twenty-first century can only be resolved through collective action and common purpose. This Article surveys some of these challenges and asks how common purpose can ever emerge on the present bioethical and regulatory landscape. The solution may lie in embracing a broader concept of autonomy that empowers individuals to protect their interests by exercising meaningful rights of data citizenship. This Article argues that twentieth-century bioethics was a paternalistic, top-down affair in which self-proclaimed ethics experts set standards to protect research subjects portrayed as autonomous yet too vulnerable and disorganized to protect themselves. The time may be ripe for BioEXIT, a popular uprising of regular people seeking a meaningful voice in establishing citizen-led ethical and privacy standards to advance big-data science while addressing the concerns people feel about the privacy of their health data. PMID:29118898

Asynchronous Object Storage with QoS for Scientific and Commercial Big Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brim, Michael J; Dillow, David A; Oral, H Sarp

2013-01-01

This paper presents our design for an asynchronous object storage system intended for use in scientific and commercial big data workloads. Use cases from the target workload do- mains are used to motivate the key abstractions used in the application programming interface (API). The architecture of the Scalable Object Store (SOS), a prototype object stor- age system that supports the API s facilities, is presented. The SOS serves as a vehicle for future research into scalable and resilient big data object storage. We briefly review our research into providing efficient storage servers capable of providing quality of service (QoS) contractsmore » relevant for big data use cases.« less

The Structural Consequences of Big Data-Driven Education.

PubMed

Zeide, Elana

2017-06-01

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.

Challenges and Potential Solutions for Big Data Implementations in Developing Countries

PubMed Central

Mayan, J.C; García, M.J.; Almerares, A.A.; Househ, M.

2014-01-01

Summary Background The volume of data, the velocity with which they are generated, and their variety and lack of structure hinder their use. This creates the need to change the way information is captured, stored, processed, and analyzed, leading to the paradigm shift called Big Data. Objectives To describe the challenges and possible solutions for developing countries when implementing Big Data projects in the health sector. Methods A non-systematic review of the literature was performed in PubMed and Google Scholar. The following keywords were used: “big data”, “developing countries”, “data mining”, “health information systems”, and “computing methodologies”. A thematic review of selected articles was performed. Results There are challenges when implementing any Big Data program including exponential growth of data, special infrastructure needs, need for a trained workforce, need to agree on interoperability standards, privacy and security issues, and the need to include people, processes, and policies to ensure their adoption. Developing countries have particular characteristics that hinder further development of these projects. Conclusions The advent of Big Data promises great opportunities for the healthcare field. In this article, we attempt to describe the challenges developing countries would face and enumerate the options to be used to achieve successful implementations of Big Data programs. PMID:25123719

Private and Efficient Query Processing on Outsourced Genomic Databases.

PubMed

Ghasemi, Reza; Al Aziz, Md Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-09-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time consuming and expensive process. Second, it requires large-scale computation and storage systems to process genomic sequences. Third, genomic databases are often owned by different organizations, and thus, not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 Single Nucleotide Polymorphisms (SNPs) in a database of 20 000 records takes around 100 and 150 s, respectively.

Private and Efficient Query Processing on Outsourced Genomic Databases

PubMed Central

Ghasemi, Reza; Al Aziz, Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-01-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time-consuming and expensive process. Second, it requires large-scale computation and storage systems to processes genomic sequences. Third, genomic databases are often owned by different organizations and thus not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 SNPs in a database of 20,000 records takes around 100 and 150 seconds, respectively. PMID:27834660

Surveillance, Big Data Analytics and the Death of Privacy

ERIC Educational Resources Information Center

Doughty, Howard A.

2014-01-01

In this article, Howard Doughty examines how today's technological devices alter and increasingly substitute for one's body/mind, sociality and (a)morality. He claims that today, under the crushing weightlessness of virtuality, citizens are less confident, more willing to retreat into the idiocy of private life. He goes on to address the…

78 FR 52553 - Privacy Act of 1974; Department of Homeland Security/ALL-035 Common Entity Index Prototype System...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-23

... data elements: Full Name; Alias(es); Gender; Date of Birth; Country of Birth; Country of Citizenship... locked drawer behind a locked door. The records may be stored on magnetic disc, tape, or digital media...

Clinical genomics, big data, and electronic medical records: reconciling patient rights with research when privacy and science collide.

PubMed

Kulynych, Jennifer; Greely, Henry T

2017-04-01

Widespread use of medical records for research, without consent, attracts little scrutiny compared to biospecimen research, where concerns about genomic privacy prompted recent federal proposals to mandate consent. This paper explores an important consequence of the proliferation of electronic health records (EHRs) in this permissive atmosphere: with the advent of clinical gene sequencing, EHR-based secondary research poses genetic privacy risks akin to those of biospecimen research, yet regulators still permit researchers to call gene sequence data 'de-identified', removing such data from the protection of the federal Privacy Rule and federal human subjects regulations. Medical centers and other providers seeking to offer genomic 'personalized medicine' now confront the problem of governing the secondary use of clinical genomic data as privacy risks escalate. We argue that regulators should no longer permit HIPAA-covered entities to treat dense genomic data as de-identified health information. Even with this step, the Privacy Rule would still permit disclosure of clinical genomic data for research, without consent, under a data use agreement, so we also urge that providers give patients specific notice before disclosing clinical genomic data for research, permitting (where possible) some degree of choice and control. To aid providers who offer clinical gene sequencing, we suggest both general approaches and specific actions to reconcile patients' rights and interests with genomic research.

Clinical genomics, big data, and electronic medical records: reconciling patient rights with research when privacy and science collide

PubMed Central

Greely, Henry T.

2017-01-01

Abstract Widespread use of medical records for research, without consent, attracts little scrutiny compared to biospecimen research, where concerns about genomic privacy prompted recent federal proposals to mandate consent. This paper explores an important consequence of the proliferation of electronic health records (EHRs) in this permissive atmosphere: with the advent of clinical gene sequencing, EHR-based secondary research poses genetic privacy risks akin to those of biospecimen research, yet regulators still permit researchers to call gene sequence data ‘de-identified’, removing such data from the protection of the federal Privacy Rule and federal human subjects regulations. Medical centers and other providers seeking to offer genomic ‘personalized medicine’ now confront the problem of governing the secondary use of clinical genomic data as privacy risks escalate. We argue that regulators should no longer permit HIPAA-covered entities to treat dense genomic data as de-identified health information. Even with this step, the Privacy Rule would still permit disclosure of clinical genomic data for research, without consent, under a data use agreement, so we also urge that providers give patients specific notice before disclosing clinical genomic data for research, permitting (where possible) some degree of choice and control. To aid providers who offer clinical gene sequencing, we suggest both general approaches and specific actions to reconcile patients’ rights and interests with genomic research. PMID:28852559

Designing a Facebook interface for senior users.

PubMed

Gomes, Gonçalo; Duarte, Carlos; Coelho, José; Matos, Eduardo

2014-01-01

The adoption of social networks by older adults has increased in recent years. However, many still cannot make use of social networks as these are simply not adapted to them. Through a series of direct observations, interviews, and focus groups, we identified recommendations for the design of social networks targeting seniors. Based on these, we developed a prototype for tablet devices, supporting sharing and viewing Facebook content. We then conducted a user study comparing our prototype with Facebook's native mobile application. We have found that Facebook's native application does not meet senior users concerns, like privacy and family focus, while our prototype, designed in accordance with the collected recommendations, supported relevant use cases in a usable and accessible manner.

Big Data in radiation therapy: challenges and opportunities.

PubMed

Lustberg, Tim; van Soest, Johan; Jochems, Arthur; Deist, Timo; van Wijk, Yvonka; Walsh, Sean; Lambin, Philippe; Dekker, Andre

2017-01-01

Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity (4Vs) of Big Data because they are spread across different care providers and not easily shared owing to patient privacy protection. The magnitude of the 4Vs is substantial in oncology, especially owing to imaging modalities and unclear data definitions. To create useful models ideally all data of all care providers are understood and learned from; however, this presents challenges in the guise of poor data quality, patient privacy concerns, geographical spread, interoperability and large volume. In radiation oncology, there are many efforts to collect data for research and innovation purposes. Clinical trials are the gold standard when proving any hypothesis that directly affects the patient. Collecting data in registries with strict predefined rules is also a common approach to find answers. A third approach is to develop data stores that can be used by modern machine learning techniques to provide new insights or answer hypotheses. We believe all three approaches have their strengths and weaknesses, but they should all strive to create Findable, Accessible, Interoperable, Reusable (FAIR) data. To learn from these data, we need distributed learning techniques, sending machine learning algorithms to FAIR data stores around the world, learning from trial data, registries and routine clinical data rather than trying to centralize all data. To improve and personalize medicine, rapid learning platforms must be able to process FAIR "Big Data" to evaluate current clinical practice and to guide further innovation.

Fall 2014 Data-Intensive Systems

DTIC Science & Technology

2014-10-29

Oct 2014 © 2014 Carnegie Mellon University Big Data Systems NoSQL and horizontal scaling are changing architecture principles by creating...University Status LEAP4BD • Ready to pilot QuABase • Prototype is complete – covers 8 NoSQL /NewSQL implementations • Completing validation testing Big...machine learning to automate population of knowledge base • Initial focus on NoSQL /NewSQL technology domain • Extend to create knowledge bases in other

How the Adoption of the Big-Data Paradigm Affects the Key Factors That Influence the Effectiveness of an Information Assurance (IA) Framework: A Multiple-Case Study

ERIC Educational Resources Information Center

Apple, Benjamin G.

2017-01-01

This qualitative study identified those factors that influence the perceived effectiveness of traditional IA control frameworks. The key factors examined in this study are risk management, governance, access control, privacy protection, integrity, availability, reliability, and usability. The researcher endeavored to determine how the…

Big Data in Education: Balancing the Benefits of Educational Research and Student Privacy. A Workshop Summary

ERIC Educational Resources Information Center

National Academy of Education, 2017

2017-01-01

This is a critical time to understand the benefits and risks of educational research using large data sets. Massive quantities of educational data can now be stored, analyzed, and shared. State longitudinal data systems can track individual students from pre-K through college and work. Districts and schools keep detailed data on individual…

Big Data and Biomedical Informatics: A Challenging Opportunity

PubMed Central

2014-01-01

Summary Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations. PMID:24853034

Big data and biomedical informatics: a challenging opportunity.

PubMed

Bellazzi, R

2014-05-22

Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

Resilient, undercontrolled, and overcontrolled personality prototypes among persons with spinal cord injury.

PubMed

Berry, Jack W; Elliott, Timothy R; Rivera, Patricia

2007-12-01

A sample of 199 persons with spinal cord injury (SCI) were assessed on Big Five personality dimensions using the NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992) at admission to an inpatient medical rehabilitation program. A cluster analysis of the baseline NEO-FFI yielded 3 cluster prototypes that resemble resilient, undercontrolled, and overcontrolled prototypes identified in many previous studies of children and adult community samples. Compared with normative samples, this sample had significantly fewer resilient prototypes and significantly more overcontrolled and undercontrolled prototypes. Undercontrolled individuals were the modal prototype. The resilient and undercontrolled types were better adjusted than the overcontrolled types, showing lower levels of depression at admission and higher acceptance of disability at discharge. The resilient type at admission predicted the most effective reports of social problem-solving abilities at discharge and the overcontrolled type the least. We discuss the implications of these results for assessment and interventions in rehabilitation settings.

Development of an Online Platform to Support the Network of Caregivers of People with Dementia.

PubMed

Verwey, Renée; van Berlo, Miranda; Duymelinck, Saskia; Willard, Sarah; van Rossum, Erik

2016-01-01

In the Netherlands, care technology is used insufficiently to support people with dementia, their family and professional caregivers. In this project we integrate a range of services and applications into an online platform, with the aim to strengthen these networks and to support communication between their members. The prototype of the platform was made in an iterative user centered way. Semi structured (group) interviews were conducted to specify the requirements. The platform consists of 'cubes' with information about dementia (care), video communication options, a calendar and a care plan. The first prototype of the platform was valued by the participants, but privacy matters and registration issues were pointed out when using a shared care plan. Additional applications to monitor health and safety will be integrated in the second prototype. This prototype will be tested on its usability, feasibility and desirability during a pilot study in spring 2016.

The dynamics of big data and human rights: the case of scientific research.

PubMed

Vayena, Effy; Tasioulas, John

2016-12-28

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities-the new capabilities and risks-of the rapidly evolving digital environment.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

The dynamics of big data and human rights: the case of scientific research

PubMed Central

Tasioulas, John

2016-01-01

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities—the new capabilities and risks—of the rapidly evolving digital environment. This article is part of the themed issue ‘The ethical impact of data science’. PMID:28336802

Evaluation of 3D printed optofluidic smart glass prototypes.

PubMed

Wolfe, Daniel; Goossen, K W

2018-01-22

Smart glass or smart windows are an innovative technology used for thermal management, energy efficiency, and privacy applications. Notable commercially available smart glass relies on an electric stimuli to modulate the glass from a transparent to a translucent mode of operation. However, the current market technologies, such as electrochromic, polymer dispersed liquid crystal, and suspended particle devices are expensive and suffer from solar absorption, poor transmittance modulation, and in some cases, continuous power consumption. The authors of this paper present a novel optofluidic smart glass prototype capable of modulating visible light transmittance from 8% to 85%.

Big data, smart homes and ambient assisted living.

PubMed

Vimarlund, V; Wass, S

2014-08-15

To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today's services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability.

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

PubMed Central

Miron-Shatz, T.; Lau, A. Y. S.; Paton, C.

2014-01-01

Summary Objectives As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful. PMID:25123717

Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.

PubMed

Hansen, M M; Miron-Shatz, T; Lau, A Y S; Paton, C

2014-08-15

As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to "small data" would also be useful.

Health and Big Data: An Ethical Framework for Health Information Collection by Corporate Wellness Programs.

PubMed

Ajunwa, Ifeoma; Crawford, Kate; Ford, Joel S

2016-09-01

This essay details the resurgence of wellness program as employed by large corporations with the aim of reducing healthcare costs. The essay narrows in on a discussion of how Big Data collection practices are being utilized in wellness programs and the potential negative impact on the worker in regards to privacy and employment discrimination. The essay offers an ethical framework to be adopted by wellness program vendors in order to conduct wellness programs that would achieve cost-saving goals without undue burdens on the worker. The essay also offers some innovative approaches to wellness that may well better serve the goals of healthcare cost reduction. © 2016 American Society of Law, Medicine & Ethics.

Privacy Issues in the Development of a Virtual Mental Health Clinic for University Students: A Qualitative Study.

PubMed

Gulliver, Amelia; Bennett, Kylie; Bennett, Anthony; Farrer, Louise M; Reynolds, Julia; Griffiths, Kathleen M

2015-01-01

There is a growing need to develop online services for university students with the capacity to complement existing services and efficiently address student mental health problems. Previous research examining the development and acceptability of online interventions has revealed that issues such as privacy critically impact user willingness to engage with these services. To explore university student perspectives on privacy issues related to using an online mental health service within the context of the development of an online, university-based virtual mental health clinic. There were two stages of data collection. The first stage consisted of four 1.5-hour focus groups conducted with university students (n=19; 10 female, 9 male, mean age = 21.6 years) to determine their ideas about the virtual clinic including privacy issues. The second stage comprised three 1-hour prototype testing sessions conducted with university students (n=6; 3 male, 3 female, mean age = 21.2 years) using participatory design methods to develop and refine a service model for the virtual clinic and determine student views on privacy within this context. The students raised a number of issues related to privacy in relation to the development of the university virtual clinic. Major topics included the types of personal information they would be willing to provide (minimal information and optional mental health data), concern about potential access to their personal data by the university, the perceived stigma associated with registering for the service, and privacy and anonymity concerns related to online forums contained within the virtual clinic. Students would be more comfortable providing personal information and engaging with the virtual clinic if they trust the privacy and security of the service. Implications of this study include building the clinic in a flexible way to accommodate user preferences.

Human Interoperability: Experimentation to Understand & Improve the Human Component of Complex Systems

DTIC Science & Technology

2008-09-30

and Accountability Act of 1996 ) prohibit health care providers from sharing certain information about patients, and the Posse Comitatus Act (1878...Publishers, pp. 333-380. Carley, K.M., and Svoboda, D.M. ( 1996 ). Modeling Organizational Adaptation as a Simulated Annealing Process. Sociological...Privacy: Interdisciplinary Frameworks and Solution. Hershey , PA: IGI Global. Salas, E., Sims, D.E., & Burke, C.S. (2005). Is there a big five in

Resilient, Undercontrolled, and Overcontrolled Personality Prototypes Among Persons With Spinal Cord Injury

PubMed Central

Berry, Jack W.; Elliott, Timothy R.; Rivera, Patricia

2008-01-01

A sample of 199 persons with spinal cord injury (SCI) were assessed on Big Five personality dimensions using the NEO Five-Factor Inventory (NEO–FFI; Costa & McCrae, 1992) at admission to an inpatient medical rehabilitation program. A cluster analysis of the baseline NEO–FFI yielded 3 cluster prototypes that resemble resilient, undercontrolled, and overcontrolled prototypes identified in many previous studies of children and adult community samples. Compared with normative samples, this sample had significantly fewer resilient prototypes and significantly more overcontrolled and undercontrolled prototypes. Undercontrolled individuals were the modal prototype. The resilient and undercontrolled types were better adjusted than the overcontrolled types, showing lower levels of depression at admission and higher acceptance of disability at discharge. The resilient type at admission predicted the most effective reports of social problem-solving abilities at discharge and the overcontrolled type the least. We discuss the implications of these results for assessment and interventions in rehabilitation settings. PMID:18001229

Leveraging the big-data revolution: CMS is expanding capabilities to spur health system transformation.

PubMed

Brennan, Niall; Oelschlaeger, Allison; Cox, Christine; Tavenner, Marilyn

2014-07-01

As the largest single payer for health care in the United States, the Centers for Medicare and Medicaid Services (CMS) generates enormous amounts of data. Historically, CMS has faced technological challenges in storing, analyzing, and disseminating this information because of its volume and privacy concerns. However, rapid progress in the fields of data architecture, storage, and analysis--the big-data revolution--over the past several years has given CMS the capabilities to use data in new and innovative ways. We describe the different types of CMS data being used both internally and externally, and we highlight a selection of innovative ways in which big-data techniques are being used to generate actionable information from CMS data more effectively. These include the use of real-time analytics for program monitoring and detecting fraud and abuse and the increased provision of data to providers, researchers, beneficiaries, and other stakeholders. Project HOPE—The People-to-People Health Foundation, Inc.

Using Browser Notebooks to Analyse Big Atmospheric Data-sets in the Cloud

NASA Astrophysics Data System (ADS)

Robinson, N.; Tomlinson, J.; Arribas, A.; Prudden, R.

2016-12-01

We are presenting an account of our experience building an ecosystem for the analysis of big atmospheric data-sets. By using modern technologies we have developed a prototype platform which is scaleable and capable of analysing very large atmospheric datasets. We tested different big-data ecosystems such as Hadoop MapReduce, Spark and Dask, in order to find the one which was best suited for analysis of multidimensional binary data such as NetCDF. We make extensive use of infrastructure-as-code and containerisation to provide a platform which is reusable, and which can scale to accommodate changes in demand. We make this platform readily accessible using browser based notebooks. As a result, analysts with minimal technology experience can, in tens of lines of Python, make interactive data-visualisation web pages, which can analyse very large amounts of data using cutting edge big-data technology

Big Data, Smart Homes and Ambient Assisted Living

PubMed Central

Wass, S.

2014-01-01

Summary Objectives To discuss how current research in the area of smart homes and ambient assisted living will be influenced by the use of big data. Methods A scoping review of literature published in scientific journals and conference proceedings was performed, focusing on smart homes, ambient assisted living and big data over the years 2011-2014. Results The health and social care market has lagged behind other markets when it comes to the introduction of innovative IT solutions and the market faces a number of challenges as the use of big data will increase. First, there is a need for a sustainable and trustful information chain where the needed information can be transferred from all producers to all consumers in a structured way. Second, there is a need for big data strategies and policies to manage the new situation where information is handled and transferred independently of the place of the expertise. Finally, there is a possibility to develop new and innovative business models for a market that supports cloud computing, social media, crowdsourcing etc. Conclusions The interdisciplinary area of big data, smart homes and ambient assisted living is no longer only of interest for IT developers, it is also of interest for decision makers as customers make more informed choices among today’s services. In the future it will be of importance to make information usable for managers and improve decision making, tailor smart home services based on big data, develop new business models, increase competition and identify policies to ensure privacy, security and liability. PMID:25123734

Open source tools for standardized privacy protection of medical images

NASA Astrophysics Data System (ADS)

Lien, Chung-Yueh; Onken, Michael; Eichelberg, Marco; Kao, Tsair; Hein, Andreas

2011-03-01

In addition to the primary care context, medical images are often useful for research projects and community healthcare networks, so-called "secondary use". Patient privacy becomes an issue in such scenarios since the disclosure of personal health information (PHI) has to be prevented in a sharing environment. In general, most PHIs should be completely removed from the images according to the respective privacy regulations, but some basic and alleviated data is usually required for accurate image interpretation. Our objective is to utilize and enhance these specifications in order to provide reliable software implementations for de- and re-identification of medical images suitable for online and offline delivery. DICOM (Digital Imaging and Communications in Medicine) images are de-identified by replacing PHI-specific information with values still being reasonable for imaging diagnosis and patient indexing. In this paper, this approach is evaluated based on a prototype implementation built on top of the open source framework DCMTK (DICOM Toolkit) utilizing standardized de- and re-identification mechanisms. A set of tools has been developed for DICOM de-identification that meets privacy requirements of an offline and online sharing environment and fully relies on standard-based methods.

Big data privacy protection model based on multi-level trusted system

NASA Astrophysics Data System (ADS)

Zhang, Nan; Liu, Zehua; Han, Hongfeng

2018-05-01

This paper introduces and inherit the multi-level trusted system model that solves the Trojan virus by encrypting the privacy of user data, and achieve the principle: "not to read the high priority hierarchy, not to write the hierarchy with low priority". Thus ensuring that the low-priority data privacy leak does not affect the disclosure of high-priority data privacy. This paper inherits the multi-level trustworthy system model of Trojan horse and divides seven different risk levels. The priority level 1˜7 represent the low to high value of user data privacy, and realize seven kinds of encryption with different execution efficiency Algorithm, the higher the priority, the greater the value of user data privacy, at the expense of efficiency under the premise of choosing a more encrypted encryption algorithm to ensure data security. For enterprises, the price point is determined by the unit equipment users to decide the length of time. The higher the risk sub-group algorithm, the longer the encryption time. The model assumes that users prefer the lower priority encryption algorithm to ensure efficiency. This paper proposes a privacy cost model for each of the seven risk subgroups. Among them, the higher the privacy cost, the higher the priority of the risk sub-group, the higher the price the user needs to pay to ensure the privacy of the data. Furthermore, by introducing the existing pricing model of economics and the human traffic model proposed by this paper and fluctuating with the market demand, this paper improves the price of unit products when the market demand is low. On the other hand, when the market demand increases, the profit of the enterprise will be guaranteed under the guidance of the government by reducing the price per unit of product. Then, this paper introduces the dynamic factors of consumers' mood and age to optimize. At the same time, seven algorithms are selected from symmetric and asymmetric encryption algorithms to define the enterprise costs at different levels. Therefore, the proposed model solves the continuous influence caused by cascading events and ensures that the disclosure of low-level data privacy of users does not affect the high-level data privacy, thus greatly improving the safety of the private information of user.

Privacy-preserving data cube for electronic medical records: An experimental evaluation.

PubMed

Kim, Soohyung; Lee, Hyukki; Chung, Yon Dohn

2017-01-01

The aim of this study is to evaluate the effectiveness and efficiency of privacy-preserving data cubes of electronic medical records (EMRs). An EMR data cube is a complex of EMR statistics that are summarized or aggregated by all possible combinations of attributes. Data cubes are widely utilized for efficient big data analysis and also have great potential for EMR analysis. For safe data analysis without privacy breaches, we must consider the privacy preservation characteristics of the EMR data cube. In this paper, we introduce a design for a privacy-preserving EMR data cube and the anonymization methods needed to achieve data privacy. We further focus on changes in efficiency and effectiveness that are caused by the anonymization process for privacy preservation. Thus, we experimentally evaluate various types of privacy-preserving EMR data cubes using several practical metrics and discuss the applicability of each anonymization method with consideration for the EMR analysis environment. We construct privacy-preserving EMR data cubes from anonymized EMR datasets. A real EMR dataset and demographic dataset are used for the evaluation. There are a large number of anonymization methods to preserve EMR privacy, and the methods are classified into three categories (i.e., global generalization, local generalization, and bucketization) by anonymization rules. According to this classification, three types of privacy-preserving EMR data cubes were constructed for the evaluation. We perform a comparative analysis by measuring the data size, cell overlap, and information loss of the EMR data cubes. Global generalization considerably reduced the size of the EMR data cube and did not cause the data cube cells to overlap, but incurred a large amount of information loss. Local generalization maintained the data size and generated only moderate information loss, but there were cell overlaps that could decrease the search performance. Bucketization did not cause cells to overlap and generated little information loss; however, the method considerably inflated the size of the EMR data cubes. The utility of anonymized EMR data cubes varies widely according to the anonymization method, and the applicability of the anonymization method depends on the features of the EMR analysis environment. The findings help to adopt the optimal anonymization method considering the EMR analysis environment and goal of the EMR analysis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

PubMed Central

Zhang, Xinzhi; Pérez-Stable, Eliseo J.; Bourne, Philip E.; Peprah, Emmanuel; Duru, O. Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S.; Wong, David W.S.; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them. PMID:28439179

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

PubMed

Zhang, Xinzhi; Pérez-Stable, Eliseo J; Bourne, Philip E; Peprah, Emmanuel; Duru, O Kenrik; Breen, Nancy; Berrigan, David; Wood, Fred; Jackson, James S; Wong, David W S; Denny, Joshua

2017-01-01

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Trends and New Directions in Software Architecture

DTIC Science & Technology

2014-10-10

frameworks  Open source  Cloud strategies  NoSQL  Machine Learning  MDD  Incremental approaches  Dashboards  Distributed development...complexity grows  NoSQL Models are not created equal 2014 Our Current Research  Lightweight Evaluation and Architecture Prototyping for Big Data

Privacy-Preserving Integration of Medical Data : A Practical Multiparty Private Set Intersection.

PubMed

Miyaji, Atsuko; Nakasho, Kazuhisa; Nishida, Shohei

2017-03-01

Medical data are often maintained by different organizations. However, detailed analyses sometimes require these datasets to be integrated without violating patient or commercial privacy. Multiparty Private Set Intersection (MPSI), which is an important privacy-preserving protocol, computes an intersection of multiple private datasets. This approach ensures that only designated parties can identify the intersection. In this paper, we propose a practical MPSI that satisfies the following requirements: The size of the datasets maintained by the different parties is independent of the others, and the computational complexity of the dataset held by each party is independent of the number of parties. Our MPSI is based on the use of an outsourcing provider, who has no knowledge of the data inputs or outputs. This reduces the computational complexity. The performance of the proposed MPSI is evaluated by implementing a prototype on a virtual private network to enable parallel computation in multiple threads. Our protocol is confirmed to be more efficient than comparable existing approaches.

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data.

PubMed

Plis, Sergey M; Sarwate, Anand D; Wood, Dylan; Dieringer, Christopher; Landis, Drew; Reed, Cory; Panta, Sandeep R; Turner, Jessica A; Shoemaker, Jody M; Carter, Kim W; Thompson, Paul; Hutchison, Kent; Calhoun, Vince D

2016-01-01

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.

COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data

PubMed Central

Plis, Sergey M.; Sarwate, Anand D.; Wood, Dylan; Dieringer, Christopher; Landis, Drew; Reed, Cory; Panta, Sandeep R.; Turner, Jessica A.; Shoemaker, Jody M.; Carter, Kim W.; Thompson, Paul; Hutchison, Kent; Calhoun, Vince D.

2016-01-01

The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and “closed” repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to “pooled-data” solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions. PMID:27594820

Infrastructure to support learning health systems: are we there yet? Innovative solutions and lessons learned from American Recovery and Reinvestment Act CER investments.

PubMed

Holve, Erin; Segal, Courtney

2014-11-01

The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure grants funded through the Recovery Act, in which four key issues emerge that can inform the evolution of learning health systems, including the importance of acknowledging the challenges of scaling specialized expertise needed to manage and run CER networks; the delicate balance between privacy protections and the utility of distributed networks; emerging community engagement strategies; and the complexities of developing a robust business model for multi-use networks.

Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics.

PubMed

Balthazar, Patricia; Harri, Peter; Prater, Adam; Safdar, Nabile M

2018-03-01

The Hippocratic oath and the Belmont report articulate foundational principles for how physicians interact with patients and research subjects. The increasing use of big data and artificial intelligence techniques demands a re-examination of these principles in light of the potential issues surrounding privacy, confidentiality, data ownership, informed consent, epistemology, and inequities. Patients have strong opinions about these issues. Radiologists have a fiduciary responsibility to protect the interest of their patients. As such, the community of radiology leaders, ethicists, and informaticists must have a conversation about the appropriate way to deal with these issues and help lead the way in developing capabilities in the most just, ethical manner possible. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

"Big data" and the electronic health record.

PubMed

Ross, M K; Wei, W; Ohno-Machado, L

2014-08-15

Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on "big data" in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to "big data" and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of "big data", and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

A planetary nervous system for social mining and collective awareness

NASA Astrophysics Data System (ADS)

Giannotti, F.; Pedreschi, D.; Pentland, A.; Lukowicz, P.; Kossmann, D.; Crowley, J.; Helbing, D.

2012-11-01

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.

A High Fidelity Driving Simulator as a Tool for Design and Evaluation of Highway Infrastructure Upgrades

DOT National Transportation Integrated Search

2007-05-01

A rapid prototyping approach was used in the driving simulation laboratory at the Western Transportation Institute (WTI) to simulate approximately 22 miles of US 191 between the Big Sky Resort community and the northern mouth of the Gallatin Canyon. ...

A mobile system for the improvement of heart failure management: Evaluation of a prototype.

PubMed

Haynes, Sarah C; Kim, Katherine K

2017-01-01

Management of heart failure is complex, often involving interaction with multiple providers, monitoring of symptoms, and numerous medications. Employing principles of user-centered design, we developed a high- fidelity prototype of a mobile system for heart failure self-management and care coordination. Participants, including both heart failure patients and health care providers, tested the mobile system during a one-hour one-on-one session with a facilitator. The facilitator interviewed participants about the strengths and weaknesses of the prototype, necessary features, and willingness to use the technology. We performed a qualitative content analysis using the transcripts of these interviews. Fourteen distinct themes were identified in the analysis. Of these themes, integration, technology literacy, memory, and organization were the most common. Privacy was the least common theme. Our study suggests that this integration is essential for adoption of a mobile system for chronic disease management and care coordination.

Prototype Vector Machine for Large Scale Semi-Supervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Kai; Kwok, James T.; Parvin, Bahram

2009-04-29

Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of themore » kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.« less

Big Data and medicine: a big deal?

PubMed

Mayer-Schönberger, V; Ingelsson, E

2018-05-01

Big Data promises huge benefits for medical research. Looking beyond superficial increases in the amount of data collected, we identify three key areas where Big Data differs from conventional analyses of data samples: (i) data are captured more comprehensively relative to the phenomenon under study; this reduces some bias but surfaces important trade-offs, such as between data quantity and data quality; (ii) data are often analysed using machine learning tools, such as neural networks rather than conventional statistical methods resulting in systems that over time capture insights implicit in data, but remain black boxes, rarely revealing causal connections; and (iii) the purpose of the analyses of data is no longer simply answering existing questions, but hinting at novel ones and generating promising new hypotheses. As a consequence, when performed right, Big Data analyses can accelerate research. Because Big Data approaches differ so fundamentally from small data ones, research structures, processes and mindsets need to adjust. The latent value of data is being reaped through repeated reuse of data, which runs counter to existing practices not only regarding data privacy, but data management more generally. Consequently, we suggest a number of adjustments such as boards reviewing responsible data use, and incentives to facilitate comprehensive data sharing. As data's role changes to a resource of insight, we also need to acknowledge the importance of collecting and making data available as a crucial part of our research endeavours, and reassess our formal processes from career advancement to treatment approval. © 2017 The Association for the Publication of the Journal of Internal Medicine.

Ethics, big data and computing in epidemiology and public health.

PubMed

Salerno, Jennifer; Knoppers, Bartha M; Lee, Lisa M; Hlaing, WayWay M; Goodman, Kenneth W

2017-05-01

This article reflects on the activities of the Ethics Committee of the American College of Epidemiology (ACE). Members of the Ethics Committee identified an opportunity to elaborate on knowledge gained since the inception of the original Ethics Guidelines published by the ACE Ethics and Standards of Practice Committee in 2000. The ACE Ethics Committee presented a symposium session at the 2016 Epidemiology Congress of the Americas in Miami on the evolving complexities of ethics and epidemiology as it pertains to "big data." This article presents a summary and further discussion of that symposium session. Three topic areas were presented: the policy implications of big data and computing, the fallacy of "secondary" data sources, and the duty of citizens to contribute to big data. A balanced perspective is needed that provides safeguards for individuals but also furthers research to improve population health. Our in-depth review offers next steps for teaching of ethics and epidemiology, as well as for epidemiological research, public health practice, and health policy. To address contemporary topics in the area of ethics and epidemiology, the Ethics Committee hosted a symposium session on the timely topic of big data. Technological advancements in clinical medicine and genetic epidemiology research coupled with rapid advancements in data networks, storage, and computation at a lower cost are resulting in the growth of huge data repositories. Big data increases concerns about data integrity; informed consent; protection of individual privacy, confidentiality, and harm; data reidentification; and the reporting of faulty inferences. Copyright © 2017 Elsevier Inc. All rights reserved.

Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

PubMed

Bates, David W; Saria, Suchi; Ohno-Machado, Lucila; Shah, Anand; Escobar, Gabriel

2014-07-01

The US health care system is rapidly adopting electronic health records, which will dramatically increase the quantity of clinical data that are available electronically. Simultaneously, rapid progress has been made in clinical analytics--techniques for analyzing large quantities of data and gleaning new insights from that analysis--which is part of what is known as big data. As a result, there are unprecedented opportunities to use big data to reduce the costs of health care in the United States. We present six use cases--that is, key examples--where some of the clearest opportunities exist to reduce costs through the use of big data: high-cost patients, readmissions, triage, decompensation (when a patient's condition worsens), adverse events, and treatment optimization for diseases affecting multiple organ systems. We discuss the types of insights that are likely to emerge from clinical analytics, the types of data needed to obtain such insights, and the infrastructure--analytics, algorithms, registries, assessment scores, monitoring devices, and so forth--that organizations will need to perform the necessary analyses and to implement changes that will improve care while reducing costs. Our findings have policy implications for regulatory oversight, ways to address privacy concerns, and the support of research on analytics. Project HOPE—The People-to-People Health Foundation, Inc.

Patient-controlled sharing of medical imaging data across unaffiliated healthcare organizations

PubMed Central

Ahn, David K; Unde, Bhagyashree; Gage, H Donald; Carr, J Jeffrey

2013-01-01

Background Current image sharing is carried out by manual transportation of CDs by patients or organization-coordinated sharing networks. The former places a significant burden on patients and providers. The latter faces challenges to patient privacy. Objective To allow healthcare providers efficient access to medical imaging data acquired at other unaffiliated healthcare facilities while ensuring strong protection of patient privacy and minimizing burden on patients, providers, and the information technology infrastructure. Methods An image sharing framework is described that involves patients as an integral part of, and with full control of, the image sharing process. Central to this framework is the Patient Controlled Access-key REgistry (PCARE) which manages the access keys issued by image source facilities. When digitally signed by patients, the access keys are used by any requesting facility to retrieve the associated imaging data from the source facility. A centralized patient portal, called a PCARE patient control portal, allows patients to manage all the access keys in PCARE. Results A prototype of the PCARE framework has been developed by extending open-source technology. The results for feasibility, performance, and user assessments are encouraging and demonstrate the benefits of patient-controlled image sharing. Discussion The PCARE framework is effective in many important clinical cases of image sharing and can be used to integrate organization-coordinated sharing networks. The same framework can also be used to realize a longitudinal virtual electronic health record. Conclusion The PCARE framework allows prior imaging data to be shared among unaffiliated healthcare facilities while protecting patient privacy with minimal burden on patients, providers, and infrastructure. A prototype has been implemented to demonstrate the feasibility and benefits of this approach. PMID:22886546

COINSTAC: Decentralizing the future of brain imaging analysis

PubMed Central

Ming, Jing; Verner, Eric; Sarwate, Anand; Kelly, Ross; Reed, Cory; Kahleck, Torran; Silva, Rogers; Panta, Sandeep; Turner, Jessica; Plis, Sergey; Calhoun, Vince

2017-01-01

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications. PMID:29123643

Wireless network system based multi-non-invasive sensors for smart home

NASA Astrophysics Data System (ADS)

Issa Ahmed, Rudhwan

There are several techniques that have been implemented for smart homes usage; however, most of these techniques are limited to a few sensors. Many of these methods neither meet the needs of the user nor are cost-effective. This thesis discusses the design, development, and implementation of a wireless network system, based on multi-non-invasive sensors for smart home environments. This system has the potential to be used as a means to accurately, and remotely, determine the activities of daily living by continuously monitoring relatively simple parameters that measure the interaction between users and their surrounding environment. We designed and developed a prototype system to meet the specific needs of the elderly population. Unlike audio-video based health monitoring systems (which have associated problems such as the encroachment of privacy), the developed system's distinct features ensure privacy and are almost invisible to the occupants, thus increasing the acceptance levels of this system in household environments. The developed system not only achieved high levels of accuracy, but it is also portable, easy to use, cost-effective, and requires low data rates and less power compared to other wireless devices such as Wi-Fi, Bluetooth, wireless USB, Ultra wideband (UWB), or Infrared (IR) wireless. Field testing of the prototype system was conducted at different locations inside and outside of the Minto Building (Centre for Advanced Studies in Engineering at Carleton University) as well as other locations, such as the washroom, kitchen, and living room of a prototype apartment. The main goal of the testing was to determine the range of the prototype system and the functionality of each sensor in different environments. After it was verified that the system operated well in all of the tested environments, data were then collected at the different locations for analysis and interpretation in order to identify the activities of daily living of an occupant.

Big Data: transforming drug development and health policy decision making.

PubMed

Alemayehu, Demissie; Berger, Marc L

The explosion of data sources, accompanied by the evolution of technology and analytical techniques, has created considerable challenges and opportunities for drug development and healthcare resource utilization. We present a systematic overview these phenomena, and suggest measures to be taken for effective integration of the new developments in the traditional medical research paradigm and health policy decision making. Special attention is paid to pertinent issues in emerging areas, including rare disease drug development, personalized medicine, Comparative Effectiveness Research, and privacy and confidentiality concerns.

“Big Data” and the Electronic Health Record

PubMed Central

Ross, M. K.; Wei, Wei

2014-01-01

Summary Objectives Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge. PMID:25123728

Testing interconnected VLSI circuits in the Big Viterbi Decoder

NASA Technical Reports Server (NTRS)

Onyszchuk, I. M.

1991-01-01

The Big Viterbi Decoder (BVD) is a powerful error-correcting hardware device for the Deep Space Network (DSN), in support of the Galileo and Comet Rendezvous Asteroid Flyby (CRAF)/Cassini Missions. Recently, a prototype was completed and run successfully at 400,000 or more decoded bits per second. This prototype is a complex digital system whose core arithmetic unit consists of 256 identical very large scale integration (VLSI) gate-array chips, 16 on each of 16 identical boards which are connected through a 28-layer, printed-circuit backplane using 4416 wires. Special techniques were developed for debugging, testing, and locating faults inside individual chips, on boards, and within the entire decoder. The methods are based upon hierarchical structure in the decoder, and require that chips or boards be wired themselves as Viterbi decoders. The basic procedure consists of sending a small set of known, very noisy channel symbols through a decoder, and matching observables against values computed by a software simulation. Also, tests were devised for finding open and short-circuited wires which connect VLSI chips on the boards and through the backplane.

Architecture and prototypical implementation of a semantic querying system for big Earth observation image bases

PubMed Central

Tiede, Dirk; Baraldi, Andrea; Sudmanns, Martin; Belgiu, Mariana; Lang, Stefan

2017-01-01

ABSTRACT Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model. PMID:29098143

Space in Space: Designing for Privacy in the Workplace

NASA Technical Reports Server (NTRS)

Akin, Jonie

2015-01-01

Privacy is cultural, socially embedded in the spatial, temporal, and material aspects of the lived experience. Definitions of privacy are as varied among scholars as they are among those who fight for their personal rights in the home and the workplace. Privacy in the workplace has become a topic of interest in recent years, as evident in discussions on Big Data as well as the shrinking office spaces in which people work. An article in The New York Times published in February of this year noted that "many companies are looking to cut costs, and one way to do that is by trimming personal space". Increasingly, organizations ranging from tech start-ups to large corporations are downsizing square footage and opting for open-office floorplans hoping to trim the budget and spark creative, productive communication among their employees. The question of how much is too much to trim when it comes to privacy, is one that is being actively addressed by the National Aeronautics and Space Administration (NASA) as they explore habitat designs for future space missions. NASA recognizes privacy as a design-related stressor impacting human health and performance. Given the challenges of sustaining life in an isolated, confined, and extreme environment such as Mars, NASA deems it necessary to determine the acceptable minimal amount for habitable volume for activities requiring at least some level of privacy in order to support optimal crew performance. Ethnographic research was conducted in 2013 to explore perceptions of privacy and privacy needs among astronauts living and working in space as part of a long-distance, long-duration mission. The allocation of space, or habitable volume, becomes an increasingly complex issue in outer space due to the costs associated with maintaining an artificial, confined environment bounded by limitations of mass while located in an extreme environment. Privacy in space, or space in space, provides a unique case study of the complex notions of privacy, the impact of design and others on achieving it, and the sensemaking that occurs when privacy is less than expected. The findings show that privacy is not just a personal, individual need but is also a need that is shared among teams and groups. Moreover, the case of space in space reveals the influence the design of the built and social environments have on privacy needs and on achieving privacy. When the level of privacy is less than expected, sensemaking occurs and the lack of privacy is dealt with by means of absencing the present. creating new social norms, and "making space" by manipulating the spatial, temporal, material aspects of the lived experience. Although the Mars habitat study represents an extreme case of privacy in the workplace, lessons learned from outer space are applicable to life in the Earth-bound workplace. A mini-case study was conducted to evaluate office space at the headquarters of a major American airline that illustrates the usefulness of building unexpected bridges between the unknown, unfamiliar Mars habitat and the everyday workplace. The comparative studies reveal insight into the interconnected, social nature of the spatial, temporal, and material aspects of the lived experience and how users of the habitat and office workspace view privacy, self, and others through an embodied, design interaction.

[Review of the methodological, ethical, legal and social issues of research projects in healthcare with big data].

PubMed

de Lecuona, Itziar

2018-05-31

The current model for reviewing research with human beings basically depends on decision-making processes within research ethics committees. These committees must be aware of the importance of the new digital paradigm based on the large-scale exploitation of datasets, including personal data on health. This article offers guidelines, with the application of the EU's General Data Protection Regulation, for the appropriate evaluation of projects that are based on the use of big data analytics in healthcare. The processes for gathering and using this data constitute a niche where current research is developed. In this context, the existing protocols for obtaining informed consent from participants are outdated, as they are based not only on the assumption that personal data are anonymized, but that they will continue to be so in the future. As a result, it is essential that research ethics committees take on new capabilities and revisit values such as privacy and freedom, updating protocols, methodologies and working procedures. This change in the work culture will provide legal security to the personnel involved in research, will make it possible to guarantee the protection of the privacy of the subjects of the data, and will permit orienting the exploitation of data to avoid the commodification of personal data in this era of deidentification, so that research meets actual social needs and not spurious or opportunistic interests disguised as research. Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Utility of Big Area Additive Manufacturing (BAAM) For The Rapid Manufacture of Customized Electric Vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Love, Lonnie J.

This Oak Ridge National Laboratory (ORNL) Manufacturing Development Facility (MDF) technical collaboration project was conducted in two phases as a CRADA with Local Motors Inc. Phase 1 was previously reported as Advanced Manufacturing of Complex Cyber Mechanical Devices through Community Engagement and Micro-manufacturing and demonstrated the integration of components onto a prototype body part for a vehicle. Phase 2 was reported as Utility of Big Area Additive Manufacturing (BAAM) for the Rapid Manufacture of Customized Electric Vehicles and demonstrated the high profile live printing of an all-electric vehicle using ONRL s Big Area Additive Manufacturing (BAAM) technology. This demonstration generatedmore » considerable national attention and successfully demonstrated the capabilities of the BAAM system as developed by ORNL and Cincinnati, Inc. and the feasibility of additive manufacturing of a full scale electric vehicle as envisioned by the CRADA partner Local Motors, Inc.« less

Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework

PubMed Central

Li, Zhenlong; Yang, Chaowei; Jin, Baoxuan; Yu, Manzhu; Liu, Kai; Sun, Min; Zhan, Matthew

2015-01-01

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists. PMID:25742012

Enabling big geoscience data analytics with a cloud-based, MapReduce-enabled and service-oriented workflow framework.

PubMed

Li, Zhenlong; Yang, Chaowei; Jin, Baoxuan; Yu, Manzhu; Liu, Kai; Sun, Min; Zhan, Matthew

2015-01-01

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.

An efficient quantum scheme for Private Set Intersection

NASA Astrophysics Data System (ADS)

Shi, Run-hua; Mu, Yi; Zhong, Hong; Cui, Jie; Zhang, Shun

2016-01-01

Private Set Intersection allows a client to privately compute set intersection with the collaboration of the server, which is one of the most fundamental and key problems within the multiparty collaborative computation of protecting the privacy of the parties. In this paper, we first present a cheat-sensitive quantum scheme for Private Set Intersection. Compared with classical schemes, our scheme has lower communication complexity, which is independent of the size of the server's set. Therefore, it is very suitable for big data services in Cloud or large-scale client-server networks.

Bidirectional active control of structures with type-2 fuzzy PD and PID

NASA Astrophysics Data System (ADS)

Paul, Satyam; Yu, Wen; Li, Xiaoou

2018-03-01

Proportional-derivative and proportional-integral-derivative (PD/PID) controllers are popular algorithms in structure vibration control. In order to maintain minimum regulation error, the PD/PID control require big proportional and derivative gains. The control performances are not satisfied because of the big uncertainties in the buildings. In this paper, type-2 fuzzy system is applied to compensate the unknown uncertainties, and is combined with the PD/PID control. We prove the stability of these fuzzy PD and PID controllers. The sufficient conditions can be used for choosing the gains of PD/PID. The theory results are verified by a two-storey building prototype. The experimental results validate our analysis.

Privacy-preserving GWAS analysis on federated genomic datasets.

PubMed

Constable, Scott D; Tang, Yuzhe; Wang, Shuang; Jiang, Xiaoqian; Chapin, Steve

2015-01-01

The biomedical community benefits from the increasing availability of genomic data to support meaningful scientific research, e.g., Genome-Wide Association Studies (GWAS). However, high quality GWAS usually requires a large amount of samples, which can grow beyond the capability of a single institution. Federated genomic data analysis holds the promise of enabling cross-institution collaboration for effective GWAS, but it raises concerns about patient privacy and medical information confidentiality (as data are being exchanged across institutional boundaries), which becomes an inhibiting factor for the practical use. We present a privacy-preserving GWAS framework on federated genomic datasets. Our method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside. We demonstrate our technique by implementing a framework for minor allele frequency counting and χ2 statistics calculation, one of typical computations used in GWAS. For efficient prototyping, we use a state-of-the-art MPC framework, i.e., Portable Circuit Format (PCF) 1. Our experimental results show promise in realizing both efficient and secure cross-institution GWAS computations.

Privacy-preserving photo sharing based on a public key infrastructure

NASA Astrophysics Data System (ADS)

Yuan, Lin; McNally, David; Küpçü, Alptekin; Ebrahimi, Touradj

2015-09-01

A significant number of pictures are posted to social media sites or exchanged through instant messaging and cloud-based sharing services. Most social media services offer a range of access control mechanisms to protect users privacy. As it is not in the best interest of many such services if their users restrict access to their shared pictures, most services keep users' photos unprotected which makes them available to all insiders. This paper presents an architecture for a privacy-preserving photo sharing based on an image scrambling scheme and a public key infrastructure. A secure JPEG scrambling is applied to protect regional visual information in photos. Protected images are still compatible with JPEG coding and therefore can be viewed by any one on any device. However, only those who are granted secret keys will be able to descramble the photos and view their original versions. The proposed architecture applies an attribute-based encryption along with conventional public key cryptography, to achieve secure transmission of secret keys and a fine-grained control over who may view shared photos. In addition, we demonstrate the practical feasibility of the proposed photo sharing architecture with a prototype mobile application, ProShare, which is built based on iOS platform.

Agent-oriented privacy-based information brokering architecture for healthcare environments.

PubMed

Masaud-Wahaishi, Abdulmutalib; Ghenniwa, Hamada

2009-01-01

Healthcare industry is facing a major reform at all levels-locally, regionally, nationally, and internationally. Healthcare services and systems become very complex and comprise of a vast number of components (software systems, doctors, patients, etc.) that are characterized by shared, distributed and heterogeneous information sources with varieties of clinical and other settings. The challenge now faced with decision making, and management of care is to operate effectively in order to meet the information needs of healthcare personnel. Currently, researchers, developers, and systems engineers are working toward achieving better efficiency and quality of service in various sectors of healthcare, such as hospital management, patient care, and treatment. This paper presents a novel information brokering architecture that supports privacy-based information gathering in healthcare. Architecturally, the brokering is viewed as a layer of services where a brokering service is modeled as an agent with a specific architecture and interaction protocol that are appropriate to serve various requests. Within the context of brokering, we model privacy in terms of the entities ability to hide or reveal information related to its identities, requests, and/or capabilities. A prototype of the proposed architecture has been implemented to support information-gathering capabilities in healthcare environments using FIPA-complaint platform JADE.

Resilient, overcontrolled, and undercontrolled personality prototypes in childhood: replicability, predictive power, and the trait-type issue.

PubMed

Asendorpf, J B; van Aken, M A

1999-10-01

In a longitudinal study, Q-sort patterns of German preschool children were analyzed for personality prototypes and related to developmental outcomes up to age 12. Q-factor analyses confirmed 3 prototypic patterns that showed a high continuity and cross-judge consistency; were similar to those found for North American, Dutch, and Icelandic children; and can be interpreted as resilient, overcontrolled, and undercontrolled. Relations reported by R. W. Robins, O. P. John, A. Caspi, T. E. Moffitt, & M. Stouthamer-Loeber (1996) between these 3 patterns and the Big Five were fully replicated. Growth curve analyses showed that the 3 patterns predicted important developmental outcomes in both the social and the cognitive domains. Evidence was found for both traits and types: A continuous dimension of resiliency bifurcates in its lower part into two relatively discrete personality types, overcontrollers and undercontrollers.

The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts.

PubMed

Mittelstadt, Brent Daniel; Floridi, Luciano

2016-04-01

The capacity to collect and analyse data is growing exponentially. Referred to as 'Big Data', this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) 'Big Data Divides' created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between 'academic' and 'commercial' Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.

Tweets and Facebook Posts, the Novelty Techniques in the Creation of Origin-Destination Models

NASA Astrophysics Data System (ADS)

Malema, H. K.; Musakwa, W.

2016-06-01

Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed.

omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling

PubMed Central

Phan, John H.; Kothari, Sonal; Wang, May D.

2016-01-01

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of “Big Data”. Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/. PMID:27532062

samiDB: A Prototype Data Archive for Big Science Exploration

NASA Astrophysics Data System (ADS)

Konstantopoulos, I. S.; Green, A. W.; Cortese, L.; Foster, C.; Scott, N.

2015-04-01

samiDB is an archive, database, and query engine to serve the spectra, spectral hypercubes, and high-level science products that make up the SAMI Galaxy Survey. Based on the versatile Hierarchical Data Format (HDF5), samiDB does not depend on relational database structures and hence lightens the setup and maintenance load imposed on science teams by metadata tables. The code, written in Python, covers the ingestion, querying, and exporting of data as well as the automatic setup of an HTML schema browser. samiDB serves as a maintenance-light data archive for Big Science and can be adopted and adapted by science teams that lack the means to hire professional archivists to set up the data back end for their projects.

Quantum solution to a class of two-party private summation problems

NASA Astrophysics Data System (ADS)

Shi, Run-Hua; Zhang, Shun

2017-09-01

In this paper, we define a class of special two-party private summation (S2PPS) problems and present a common quantum solution to S2PPS problems. Compared to related classical solutions, our solution has advantages of higher security and lower communication complexity, and especially it can ensure the fairness of two parties without the help of a third party. Furthermore, we investigate the practical applications of our proposed S2PPS protocol in many privacy-preserving settings with big data sets, including private similarity decision, anonymous authentication, social networks, secure trade negotiation, secure data mining.

Large-scale Health Information Database and Privacy Protection.

PubMed

Yamamoto, Ryuichi

2016-09-01

Japan was once progressive in the digitalization of healthcare fields but unfortunately has fallen behind in terms of the secondary use of data for public interest. There has recently been a trend to establish large-scale health databases in the nation, and a conflict between data use for public interest and privacy protection has surfaced as this trend has progressed. Databases for health insurance claims or for specific health checkups and guidance services were created according to the law that aims to ensure healthcare for the elderly; however, there is no mention in the act about using these databases for public interest in general. Thus, an initiative for such use must proceed carefully and attentively. The PMDA projects that collect a large amount of medical record information from large hospitals and the health database development project that the Ministry of Health, Labour and Welfare (MHLW) is working on will soon begin to operate according to a general consensus; however, the validity of this consensus can be questioned if issues of anonymity arise. The likelihood that researchers conducting a study for public interest would intentionally invade the privacy of their subjects is slim. However, patients could develop a sense of distrust about their data being used since legal requirements are ambiguous. Nevertheless, without using patients' medical records for public interest, progress in medicine will grind to a halt. Proper legislation that is clear for both researchers and patients will therefore be highly desirable. A revision of the Act on the Protection of Personal Information is currently in progress. In reality, however, privacy is not something that laws alone can protect; it will also require guidelines and self-discipline. We now live in an information capitalization age. I will introduce the trends in legal reform regarding healthcare information and discuss some basics to help people properly face the issue of health big data and privacy protection with a sense of ownership.

Large-scale Health Information Database and Privacy Protection*1

PubMed Central

YAMAMOTO, Ryuichi

2016-01-01

Japan was once progressive in the digitalization of healthcare fields but unfortunately has fallen behind in terms of the secondary use of data for public interest. There has recently been a trend to establish large-scale health databases in the nation, and a conflict between data use for public interest and privacy protection has surfaced as this trend has progressed. Databases for health insurance claims or for specific health checkups and guidance services were created according to the law that aims to ensure healthcare for the elderly; however, there is no mention in the act about using these databases for public interest in general. Thus, an initiative for such use must proceed carefully and attentively. The PMDA*2 projects that collect a large amount of medical record information from large hospitals and the health database development project that the Ministry of Health, Labour and Welfare (MHLW) is working on will soon begin to operate according to a general consensus; however, the validity of this consensus can be questioned if issues of anonymity arise. The likelihood that researchers conducting a study for public interest would intentionally invade the privacy of their subjects is slim. However, patients could develop a sense of distrust about their data being used since legal requirements are ambiguous. Nevertheless, without using patients’ medical records for public interest, progress in medicine will grind to a halt. Proper legislation that is clear for both researchers and patients will therefore be highly desirable. A revision of the Act on the Protection of Personal Information is currently in progress. In reality, however, privacy is not something that laws alone can protect; it will also require guidelines and self-discipline. We now live in an information capitalization age. I will introduce the trends in legal reform regarding healthcare information and discuss some basics to help people properly face the issue of health big data and privacy protection with a sense of ownership. PMID:28299244

How Big Data Informs Us About Cataract Surgery: The LXXII Edward Jackson Memorial Lecture.

PubMed

Coleman, Anne Louise

2015-12-01

To characterize the role of Big Data in evaluating quality of care in ophthalmology, to highlight opportunities for studying quality improvement using data available in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry, and to show how Big Data informs us about rare events such as endophthalmitis after cataract surgery. Review of published studies, analysis of public-use Medicare claims files from 2010 to 2013, and analysis of IRIS Registry from 2013 to 2014. Statistical analysis of observational data. The overall rate of endophthalmitis after cataract surgery was 0.14% in 216 703 individuals in the Medicare database. In the IRIS Registry the endophthalmitis rate after cataract surgery was 0.08% among 511 182 individuals. Endophthalmitis rates tended to be higher in eyes with combined cataract surgery and anterior vitrectomy (P = .051), although only 0.08% of eyes had this combined procedure. Visual acuity (VA) in the IRIS Registry in eyes with and without postoperative endophthalmitis measured 1-7 days postoperatively were logMAR 0.58 (standard deviation [SD]: 0.84) (approximately Snellen acuity of 20/80) and logMAR 0.31 (SD: 0.34) (approximately Snellen acuity of 20/40), respectively. In 33 547 eyes with postoperative VA after cataract surgery, 18.3% had 1-month-postoperative VA worse than 20/40. Big Data drawing on Medicare claims and IRIS Registry records can help identify additional areas for quality improvement, such as in the 18.3% of eyes in the IRIS Registry having 1-month-postoperative VA worse than 20/40. The ability to track patient outcomes in Big Data sets provides opportunities for further research on rare complications such as postoperative endophthalmitis and outcomes from uncommon procedures such as cataract surgery combined with anterior vitrectomy. But privacy and data-security concerns associated with Big Data should not be taken lightly. Copyright © 2015 Elsevier Inc. All rights reserved.

Addressing Data Veracity in Big Data Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aman, Saima; Chelmis, Charalampos; Prasanna, Viktor

Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describemore » a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.« less

Open access, library and publisher competition, and the evolution of general commerce.

PubMed

Odlyzko, Andrew M

2015-02-01

Discussions of the economics of scholarly communication are usually devoted to Open Access, rising journal prices, publisher profits, and boycotts. That ignores what seems a much more important development in this market. Publishers, through the oft-reviled Big Deal packages, are providing much greater and more egalitarian access to the journal literature, an approximation to true Open Access. In the process, they are also marginalizing libraries and obtaining a greater share of the resources going into scholarly communication. This is enabling a continuation of publisher profits as well as of what for decades has been called "unsustainable journal price escalation." It is also inhibiting the spread of Open Access and potentially leading to an oligopoly of publishers controlling distribution through large-scale licensing. The Big Deal practices are worth studying for several general reasons. The degree to which publishers succeed in diminishing the role of libraries may be an indicator of the degree and speed at which universities transform themselves. More importantly, these Big Deals appear to point the way to the future of the whole economy, where progress is characterized by declining privacy, increasing price discrimination, increasing opaqueness in pricing, increasing reliance on low-paid or unpaid work of others for profits, and business models that depend on customer inertia. © The Author(s) 2014.

An Industrial Perspective of CAM/ROB Fuzzy Integrated Postprocessing Implementation for Redundant Robotic Workcells Applicability for Big Volume Prototyping

NASA Astrophysics Data System (ADS)

Andrés, J.; Gracia, L.; Tornero, J.; García, J. A.; González, F.

2009-11-01

The implementation of a postprocessor for the NX™ platform (Siemens Corp.) is described in this paper. It is focused on a milling redundant robotic milling workcell consisting of one KUKA KR 15/2 manipulator (6 rotary joints, KRC2 controller) mounted on a linear axis and synchronized with a rotary table (i.e., two additional joints). For carrying out a milling task, a choice among a set of possible configurations is required, taking into account the ability to avoid singular configurations by using both additional joints. Usually, experience and knowledge of the workman allow an efficient control in these cases, but being it a tedious job. Similarly to this expert knowledge, a stand-alone fuzzy controller has been programmed with Matlab's Fuzzy Logic Toolbox (The MathWorks, Inc.). Two C++ programs complement the translation of the toolpath tracking (expressed in the Cartesian space) from the NX™-CAM module into KRL (KUKA Robot Language). In order to avoid singularities or joint limits, the location of the robot and the workpiece during the execution of the task is fit after an inverse kinematics position analysis and a fuzzy inference (i.e., fuzzy criterion in the Joint Space). Additionally, the applicability of robot arms for the manufacture of big volume prototypes with this technique is proven by means of one case studied. It consists of a big orographic model to simulate floodways, return flows and retention storage of a reservoir in the Mijares river (Puebla de Arenoso, Spain). This article deals with the problem for a constant tool orientation milling process and sets the technological basis for future research at five axis milling operations.

Pellet to Part Manufacturing System for CNCs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roschli, Alex C.; Love, Lonnie J.; Post, Brian K.

Oak Ridge National Laboratory’s Manufacturing Demonstration Facility worked with Hybrid Manufacturing Technologies to develop a compact prototype composite additive manufacturing head that can effectively extrude injection molding pellets. The head interfaces with conventional CNC machine tools enabling rapid conversion of conventional machine tools to additive manufacturing tools. The intent was to enable wider adoption of Big Area Additive Manufacturing (BAAM) technology and combine BAAM technology with conventional machining systems.

An Identity-Based Anti-Quantum Privacy-Preserving Blind Authentication in Wireless Sensor Networks.

PubMed

Zhu, Hongfei; Tan, Yu-An; Zhu, Liehuang; Wang, Xianmin; Zhang, Quanxin; Li, Yuanzhang

2018-05-22

With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people's lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size.

An Identity-Based Anti-Quantum Privacy-Preserving Blind Authentication in Wireless Sensor Networks

PubMed Central

Zhu, Hongfei; Tan, Yu-an; Zhu, Liehuang; Wang, Xianmin; Zhang, Quanxin; Li, Yuanzhang

2018-01-01

With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size. PMID:29789475

Predicting nurses' acceptance of radiofrequency identification technology.

PubMed

Norten, Adam

2012-10-01

The technology of radiofrequency identification allows for the scanning of radiofrequency identification-tagged objects and individuals without line-of-sight requirements. Healthcare organizations use radiofrequency identification to ensure the health and safety of patients and medical personnel and to uncover inefficiencies. Although the successful implementation of a system incorporating radiofrequency identification technologies requires acceptance and use of the technology, some nurses using radiofrequency identification in hospitals feel like "Big Brother" is watching them. This predictive study used a theoretical model assessing the effect of five independent variables: privacy concerns, attitudes, subjective norms, controllability, and self-efficacy, on a dependent variable, nurses' behavioral intention to use radiofrequency identification. A Web-based questionnaire containing previously validated questions was answered by 106 US RNs. Multiple linear regression showed that all constructs together accounted for 60% of the variance in nurses' intention to use radiofrequency identification. Of the predictors in the model, attitudes provided the largest unique contribution when the other predictors in the model were held constant; subjective norms also provided a unique contribution. Privacy concerns, controllability, and self-efficacy did not provide a significant contribution to nurses' behavioral intention to use radiofrequency identification.

History-Enriched Spaces for Shared Encounters

NASA Astrophysics Data System (ADS)

Konomi, Shin'ichi; Sezaki, Kaoru; Kitsuregawa, Masaru

We discuss "history-enriched spaces" that use historical data to support shared encounters. We first examine our experiences with DeaiExplorer, a social network display that uses RFID and a historical database to support social interactions at academic conferences. This leads to our discussions on three complementary approaches to addressing the issues of supporting social encounters: (1) embedding historical data in embodied interactions, (2) designing for weakly involved interactions such as social navigation, and (3) designing for privacy. Finally, we briefly describe a preliminary prototype of a proxemics-based awareness tool that considers these approaches.

Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data.

PubMed

Yildirim, Oktay; Gottwald, Matthias; Schüler, Peter; Michel, Martin C

2016-01-01

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public-private partnerships, adaptive designs and big data. Public-private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development.

Opportunities and Challenges for Drug Development: Public–Private Partnerships, Adaptive Designs and Big Data

PubMed Central

Yildirim, Oktay; Gottwald, Matthias; Schüler, Peter; Michel, Martin C.

2016-01-01

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public–private partnerships, adaptive designs and big data. Public–private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development. PMID:27999543

Utilizing HDF4 File Content Maps for the Cloud

NASA Technical Reports Server (NTRS)

Lee, Hyokyung Joe

2016-01-01

We demonstrate a prototype study that HDF4 file content map can be used for efficiently organizing data in cloud object storage system to facilitate cloud computing. This approach can be extended to any binary data formats and to any existing big data analytics solution powered by cloud computing because HDF4 file content map project started as long term preservation of NASA data that doesn't require HDF4 APIs to access data.

From big data analysis in the cloud to robotic pot drumming: tales from the Met Office Informatics Lab

NASA Astrophysics Data System (ADS)

Robinson, Niall; Tomlinson, Jacob; Prudden, Rachel; Hilson, Alex; Arribas, Alberto

2017-04-01

The Met Office Informatics Lab is a small multidisciplinary team which sits between science, technology and design. Our mission is simply "to make Met Office data useful" - a deliberately broad objective. Our prototypes often trial cutting edge technologies, and so far have included projects such as virtual reality data visualisation in the web browser, bots and natural language interfaces, and artificially intelligent weather warnings. In this talk we focus on our latest project, Jade, a big data analysis platform in the cloud. It is a powerful, flexible and simple to use implementation which makes extensive use of technologies such as Jupyter, Dask, containerisation, Infrastructure as Code, and auto-scaling. Crucially, Jade is flexible enough to be used for a diverse set of applications: it can present weather forecast information to meteorologists and allow climate scientists to analyse big data sets, but it is also effective for analysing non-geospatial data. As well as making data useful, the Informatics Lab also trials new working practises. In this presentation, we will talk about our experience of making a group like the Lab successful.

Safety vs. privacy: elderly persons' experiences of a mobile safety alarm.

PubMed

Melander-Wikman, Anita; Fältholm, Ylva; Gard, Gunvor

2008-07-01

The demographic development indicates an increased elderly population in Sweden in the future. One of the greatest challenges for a society with an ageing population is to provide high-quality health and social care. New information and communication technology and services can be used to further improve health care. To enable elderly persons to stay at home as long as possible, various kinds of technology, such as safety alarms, are used at home. The aim of this study was to describe the experiences of elderly persons through testing a mobile safety alarm and their reasoning about safety, privacy and mobility. The mobile safety alarm tested was a prototype in development. Five elderly persons with functional limitations and four healthy elderly persons from a pensioner's organisation tested the alarm. The mobile alarm with a drop sensor and a positioning device was tested for 6 weeks. This intervention was evaluated with qualitative interviews, and analysed with latent content analysis. The result showed four main categories: feeling safe, being positioned and supervised, being mobile, and reflecting on new technology. From these categories, the overarching category 'Safety and mobility are more important than privacy' emerged. The mobile safety alarm was perceived to offer an increased opportunity for mobility in terms of being more active and as an aid for self-determination. The fact that the informants were located by means of the positioning device was not experienced as violating privacy as long as they could decide how to use the alarm. It was concluded that this mobile safety alarm was experienced as a tool to be active and mobile. As a way to keep self-determination and empowerment, the individual has to make a 'cost-benefit' analysis where privacy is sacrificed to the benefit of mobility and safety. The participants were actively contributing to the development process.

The community-driven BiG CZ software system for integration and analysis of bio- and geoscience data in the critical zone

NASA Astrophysics Data System (ADS)

Aufdenkampe, A. K.; Mayorga, E.; Horsburgh, J. S.; Lehnert, K. A.; Zaslavsky, I.; Valentine, D. W., Jr.; Richard, S. M.; Cheetham, R.; Meyer, F.; Henry, C.; Berg-Cross, G.; Packman, A. I.; Aronson, E. L.

2014-12-01

Here we present the prototypes of a new scientific software system designed around the new Observations Data Model version 2.0 (ODM2, https://github.com/UCHIC/ODM2) to substantially enhance integration of biological and Geological (BiG) data for Critical Zone (CZ) science. The CZ science community takes as its charge the effort to integrate theory, models and data from the multitude of disciplines collectively studying processes on the Earth's surface. The central scientific challenge of the CZ science community is to develop a "grand unifying theory" of the critical zone through a theory-model-data fusion approach, for which the key missing need is a cyberinfrastructure for seamless 4D visual exploration of the integrated knowledge (data, model outputs and interpolations) from all the bio and geoscience disciplines relevant to critical zone structure and function, similar to today's ability to easily explore historical satellite imagery and photographs of the earth's surface using Google Earth. This project takes the first "BiG" steps toward answering that need. The overall goal of this project is to co-develop with the CZ science and broader community, including natural resource managers and stakeholders, a web-based integration and visualization environment for joint analysis of cross-scale bio and geoscience processes in the critical zone (BiG CZ), spanning experimental and observational designs. We will: (1) Engage the CZ and broader community to co-develop and deploy the BiG CZ software stack; (2) Develop the BiG CZ Portal web application for intuitive, high-performance map-based discovery, visualization, access and publication of data by scientists, resource managers, educators and the general public; (3) Develop the BiG CZ Toolbox to enable cyber-savvy CZ scientists to access BiG CZ Application Programming Interfaces (APIs); and (4) Develop the BiG CZ Central software stack to bridge data systems developed for multiple critical zone domains into a single metadata catalog. The entire BiG CZ Software system is being developed on public repositories as a modular suite of open source software projects. It will be built around a new Observations Data Model Version 2.0 (ODM2) that has been developed by members of the BiG CZ project team, with community input, under separate funding.

Privacy and security in the era of digital health: what should translational researchers know and do about it?

PubMed Central

Filkins, Barbara L; Kim, Ju Young; Roberts, Bruce; Armstrong, Winston; Miller, Mark A; Hultner, Michael L; Castillo, Anthony P; Ducom, Jean-Christophe; Topol, Eric J; Steinhubl, Steven R

2016-01-01

The rapid growth in the availability and incorporation of digital technologies in almost every aspect of our lives creates extraordinary opportunities but brings with it unique challenges. This is especially true for the translational researcher, whose work has been markedly enhanced through the capabilities of big data aggregation and analytics, wireless sensors, online study enrollment, mobile engagement, and much more. At the same time each of these tools brings distinctive security and privacy issues that most translational researchers are inadequately prepared to deal with despite accepting overall responsibility for them. For the researcher, the solution for addressing these challenges is both simple and complex. Cyber-situational awareness is no longer a luxury-it is fundamental in combating both the elite and highly organized adversaries on the Internet as well as taking proactive steps to avoid a careless turn down the wrong digital dark alley. The researcher, now responsible for elements that may/may not be beyond his or her direct control, needs an additional level of cyber literacy to understand the responsibilities imposed on them as data owner. Responsibility lies with knowing what you can do about the things you can control and those you can’t. The objective of this paper is to describe the data privacy and security concerns that translational researchers need to be aware of, and discuss the tools and techniques available to them to help minimize that risk. PMID:27186282

Privacy and security in the era of digital health: what should translational researchers know and do about it?

PubMed

Filkins, Barbara L; Kim, Ju Young; Roberts, Bruce; Armstrong, Winston; Miller, Mark A; Hultner, Michael L; Castillo, Anthony P; Ducom, Jean-Christophe; Topol, Eric J; Steinhubl, Steven R

2016-01-01

The rapid growth in the availability and incorporation of digital technologies in almost every aspect of our lives creates extraordinary opportunities but brings with it unique challenges. This is especially true for the translational researcher, whose work has been markedly enhanced through the capabilities of big data aggregation and analytics, wireless sensors, online study enrollment, mobile engagement, and much more. At the same time each of these tools brings distinctive security and privacy issues that most translational researchers are inadequately prepared to deal with despite accepting overall responsibility for them. For the researcher, the solution for addressing these challenges is both simple and complex. Cyber-situational awareness is no longer a luxury-it is fundamental in combating both the elite and highly organized adversaries on the Internet as well as taking proactive steps to avoid a careless turn down the wrong digital dark alley. The researcher, now responsible for elements that may/may not be beyond his or her direct control, needs an additional level of cyber literacy to understand the responsibilities imposed on them as data owner. Responsibility lies with knowing what you can do about the things you can control and those you can't. The objective of this paper is to describe the data privacy and security concerns that translational researchers need to be aware of, and discuss the tools and techniques available to them to help minimize that risk.

Secure Secondary Use of Clinical Data with Cloud-based NLP Services. Towards a Highly Scalable Research Infrastructure.

PubMed

Christoph, J; Griebel, L; Leb, I; Engel, I; Köpcke, F; Toddenroth, D; Prokosch, H-U; Laufer, J; Marquardt, K; Sedlmayr, M

2015-01-01

The secondary use of clinical data provides large opportunities for clinical and translational research as well as quality assurance projects. For such purposes, it is necessary to provide a flexible and scalable infrastructure that is compliant with privacy requirements. The major goals of the cloud4health project are to define such an architecture, to implement a technical prototype that fulfills these requirements and to evaluate it with three use cases. The architecture provides components for multiple data provider sites such as hospitals to extract free text as well as structured data from local sources and de-identify such data for further anonymous or pseudonymous processing. Free text documentation is analyzed and transformed into structured information by text-mining services, which are provided within a cloud-computing environment. Thus, newly gained annotations can be integrated along with the already available structured data items and the resulting data sets can be uploaded to a central study portal for further analysis. Based on the architecture design, a prototype has been implemented and is under evaluation in three clinical use cases. Data from several hundred patients provided by a University Hospital and a private hospital chain have already been processed. Cloud4health has shown how existing components for secondary use of structured data can be complemented with text-mining in a privacy compliant manner. The cloud-computing paradigm allows a flexible and dynamically adaptable service provision that facilitates the adoption of services by data providers without own investments in respective hardware resources and software tools.

Genomic Sequencing: Assessing The Health Care System, Policy, And Big-Data Implications

PubMed Central

Phillips, Kathryn A.; Trosman, Julia; Kelley, Robin K.; Pletcher, Mark J.; Douglas, Michael P.; Weldon, Christine B.

2014-01-01

New genomic sequencing technologies enable the high-speed analysis of multiple genes simultaneously, including all of those in a person's genome. Sequencing is a prominent example of a “big data” technology because of the massive amount of information it produces and its complexity, diversity, and timeliness. Our objective in this article is to provide a policy primer on sequencing and illustrate how it can affect health care system and policy issues. Toward this end, we developed an easily applied classification of sequencing based on inputs, methods, and outputs. We used it to examine the implications of sequencing for three health care system and policy issues: making care more patient-centered, developing coverage and reimbursement policies, and assessing economic value. We conclude that sequencing has great promise but that policy challenges include how to optimize patient engagement as well as privacy, develop coverage policies that distinguish research from clinical uses and account for bioinformatics costs, and determine the economic value of sequencing through complex economic models that take into account multiple findings and downstream costs. PMID:25006153

Genomic sequencing: assessing the health care system, policy, and big-data implications.

PubMed

Phillips, Kathryn A; Trosman, Julia R; Kelley, Robin K; Pletcher, Mark J; Douglas, Michael P; Weldon, Christine B

2014-07-01

New genomic sequencing technologies enable the high-speed analysis of multiple genes simultaneously, including all of those in a person's genome. Sequencing is a prominent example of a "big data" technology because of the massive amount of information it produces and its complexity, diversity, and timeliness. Our objective in this article is to provide a policy primer on sequencing and illustrate how it can affect health care system and policy issues. Toward this end, we developed an easily applied classification of sequencing based on inputs, methods, and outputs. We used it to examine the implications of sequencing for three health care system and policy issues: making care more patient-centered, developing coverage and reimbursement policies, and assessing economic value. We conclude that sequencing has great promise but that policy challenges include how to optimize patient engagement as well as privacy, develop coverage policies that distinguish research from clinical uses and account for bioinformatics costs, and determine the economic value of sequencing through complex economic models that take into account multiple findings and downstream costs. Project HOPE—The People-to-People Health Foundation, Inc.

Ancient prototypes of the big bang and the Hot Universe (to the prehistory of some fundamental ideas in cosmology)

NASA Astrophysics Data System (ADS)

Eremeeva, A. I.

Gamov's cosmological (as a matter of fact, cosmophysical!) Hot Universe theory, which shocked the astrophysics by its unusual universality at first, had "predecessors" in the antiquity. In spite of the ancient natural philosophers' poor scientific knowledge, their apprehension of the world as a whole helped them to catch some profound analogies and to express some surprising guesses concerning the process of the birth of the Universe.

Pre-layout AC decoupling analysis with Mentor Graphics HyperLynx

NASA Astrophysics Data System (ADS)

Hnatiuc, Mihaela; Iov, Cǎtǎlin J.

2015-02-01

Considerable resources have been used since the humans got interested to discover the world around. Any discovery and science advance was taken tremendously amount of time, money, sometimes lives. All of these define the cost of a discovery, developing process. Getting back to electronics, this field faced in the last 20-30 years, a big boom in terms of technologies and opportunities. Thousands of equipment were developed and placed on the market. The big difference between various competitors is made at the moment by that we call the time to market. A mobile, for instance, has a time to market of around 6 months and the tendency is to have it smaller than that. That means between the concept and the first model sale, no more than 6 months should be passing. That is why new approaches are needed. The one extensively used now is the simulation. We call the simulation virtual prototyping. The virtual prototyping takes into account more than the components only. It takes into account some other project parameters that would affect the final product. Certified tools can handle such analysis. In our paper we present the case of HyperLynx, a concept developed by Mentor Graphics Company, assisting the hardware designer throughout the designing process, from thermal point of view. A test case board was analyzed at the pre-layout stage and the results presented.

Enabling secure, distributed collaborations for adrenal tumor research.

PubMed

Stell, Anthony; Sinnott, Richard; Jiang, Jipu

2010-01-01

Many e-Health strategies rely on the secure integration of datasets that have previously resided in isolated locations, but can now in principle be accessed over the Internet. Of paramount importance in the health domain is the need for the security and privacy of data that is transmitted across these networks. One such collaboration, which spans several specialist centres across France, Germany, Italy and the UK, is ENSAT - the European Network for the Study of Adrenal Tumors. The rarity of the tumors under study means the value of accessing, aggregating and comparing data from many centres is great indeed. However this is especially challenging given that ENSAT require clinical and genomic data to be seamlessly linked, but in such a way that the information governance, ethics and privacy concerns of the patients and associated stakeholders involved are visibly satisfied. Key to this is the clear separation of clinical and genomic data sets and support for rigorous patient-identity protecting access control. This is especially challenging when such data sets exist across different organisational boundaries. In this paper we describe a prototype solution offering a security-oriented tailored portal supported by a layered encryption-driven linkage technology (VANGUARD) that offers precisely such patient-privacy protecting capabilities. We describe the architecture, implementation and use to date of this facility to support the ENSAT adrenal cancer research network.

Quality of Big Data in health care.

PubMed

Sukumar, Sreenivas R; Natarajan, Ramachandran; Ferrell, Regina K

2015-01-01

The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era

NASA Astrophysics Data System (ADS)

Kuo, Kwo-Sen; Seablom, Michael; Clune, Thomas; Ramachandran, Rahul

2014-05-01

A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code. We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higher-quality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era

NASA Technical Reports Server (NTRS)

Kuo, Kwo-Sen; Seablom, Michael; Clune, Thomas; Ramachandran, Rahul

2014-01-01

A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Creating a Team Archive During Fast-Paced Anomaly Response Activities in Space Missions

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Hicks, LaDessa; Overland, David; Thronesbery, Carroll; Christofferesen, Klaus; Chow, Renee

2002-01-01

This paper describes a Web-based system to support the temporary Anomaly Response Team formed from distributed subteams in Space Shuttle and International Space Station missions. The system was designed for easy and flexible creation of small collections of files and links associated with work on a particular anomaly. The system supports privacy and levels of formality for the subteams. First we describe the supported groups and an anomaly response scenario. Then we describe the support system prototype, the Anomaly Response Tracking and Integration System (ARTIS). Finally, we describe our evaluation approach and the results of the evaluation.

Prototype of a laser guide star wavefront sensor for the Extremely Large Telescope

NASA Astrophysics Data System (ADS)

Patti, M.; Lombini, M.; Schreiber, L.; Bregoli, G.; Arcidiacono, C.; Cosentino, G.; Diolaiti, E.; Foppiani, I.

2018-06-01

The new class of large telescopes, like the future Extremely Large Telescope (ELT), are designed to work with a laser guide star (LGS) tuned to a resonance of atmospheric sodium atoms. This wavefront sensing technique presents complex issues when applied to big telescopes for many reasons, mainly linked to the finite distance of the LGS, the launching angle, tip-tilt indetermination and focus anisoplanatism. The implementation of a laboratory prototype for the LGS wavefront sensor (WFS) at the beginning of the phase study of MAORY (Multi-conjugate Adaptive Optics Relay) for ELT first light has been indispensable in investigating specific mitigation strategies for the LGS WFS issues. This paper presents the test results of the LGS WFS prototype under different working conditions. The accuracy within which the LGS images are generated on the Shack-Hartmann WFS has been cross-checked with the MAORY simulation code. The experiments show the effect of noise on centroiding precision, the impact of LGS image truncation on wavefront sensing accuracy as well as the temporal evolution of the sodium density profile and LGS image under-sampling.

Distributed Adaptive Binary Quantization for Fast Nearest Neighbor Search.

PubMed

Xianglong Liu; Zhujin Li; Cheng Deng; Dacheng Tao

2017-11-01

Hashing has been proved an attractive technique for fast nearest neighbor search over big data. Compared with the projection based hashing methods, prototype-based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure. However, existing prototype-based methods, such as spherical hashing and K-means hashing, still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube. To address this problem, we propose an adaptive binary quantization (ABQ) method that learns a discriminative hash function with prototypes associated with small unique binary codes. Our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes, and enjoys the fast training linear to the number of the training data. We further devise a distributed framework for the large-scale learning, which can significantly speed up the training of ABQ in the distributed environment that has been widely deployed in many areas nowadays. The extensive experiments on four large-scale (up to 80 million) data sets demonstrate that our method significantly outperforms state-of-the-art hashing methods, with up to 58.84% performance gains relatively.

A secure biometrics-based authentication scheme for telecare medicine information systems.

PubMed

Yan, Xiaopeng; Li, Weiheng; Li, Ping; Wang, Jiantao; Hao, Xinhong; Gong, Peng

2013-10-01

The telecare medicine information system (TMIS) allows patients and doctors to access medical services or medical information at remote sites. Therefore, it could bring us very big convenient. To safeguard patients' privacy, authentication schemes for the TMIS attracted wide attention. Recently, Tan proposed an efficient biometrics-based authentication scheme for the TMIS and claimed their scheme could withstand various attacks. However, in this paper, we point out that Tan's scheme is vulnerable to the Denial-of-Service attack. To enhance security, we also propose an improved scheme based on Tan's work. Security and performance analysis shows our scheme not only could overcome weakness in Tan's scheme but also has better performance.

Act-Frequency Signatures of the Big Five.

PubMed

Chapman, Benjamin P; Goldberg, Lewis R

2017-10-01

The traditional focus of work on personality and behavior has tended toward "major outcomes" such as health or antisocial behavior, or small sets of behaviors observable over short periods in laboratories or in convenience samples. In a community sample, we examined a wide set (400) of mundane, incidental or "every day" behavioral acts, the frequencies of which were reported over the past year. Using an exploratory methodology similar to genomic approaches (relying on the False Discovery Rate) revealed 26 prototypical acts for Intellect, 24 acts for Extraversion, 13 for Emotional Stability, nine for Conscientiousness, and six for Agreeableness. Many links were consistent with general intuition-for instance, low Conscientiousness with work and procrastination. Some of the most robust associations, however, were for acts too specific for a priori hypothesis. For instance, Extraversion was strongly associated with telling dirty jokes, Intellect with "loung[ing] around [the] house without clothes on", and Agreeableness with singing in the shower. Frequency categories for these acts changed with markedly non-linearity across Big Five Z-scores. Findings may help ground trait scores in emblematic acts, and enrich understanding of mundane or common behavioral signatures of the Big Five.

TopoLens: Building a cyberGIS community data service for enhancing the usability of high-resolution National Topographic datasets

USGS Publications Warehouse

Hu, Hao; Hong, Xingchen; Terstriep, Jeff; Liu, Yan; Finn, Michael P.; Rush, Johnathan; Wendel, Jeffrey; Wang, Shaowen

2016-01-01

Geospatial data, often embedded with geographic references, are important to many application and science domains, and represent a major type of big data. The increased volume and diversity of geospatial data have caused serious usability issues for researchers in various scientific domains, which call for innovative cyberGIS solutions. To address these issues, this paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Through the collaboration between the CyberGIS Center at the University of Illinois at Urbana-Champaign (UIUC) and the U.S. Geological Survey (USGS), a community data service for accessing, customizing, and sharing digital elevation model (DEM) and its derived datasets from the 10-meter national elevation dataset, namely TopoLens, is created to demonstrate the workflow integration of geospatial big data sources, computation, analysis needed for customizing the original dataset for end user needs, and a friendly online user environment. TopoLens provides online access to precomputed and on-demand computed high-resolution elevation data by exploiting the ROGER supercomputer. The usability of this prototype service has been acknowledged in community evaluation.

Towards pervasive computing in health care - a literature review.

PubMed

Orwat, Carsten; Graefe, Andreas; Faulwasser, Timm

2008-06-19

The evolving concepts of pervasive computing, ubiquitous computing and ambient intelligence are increasingly influencing health care and medicine. Summarizing published research, this literature review provides an overview of recent developments and implementations of pervasive computing systems in health care. It also highlights some of the experiences reported in deployment processes. There is no clear definition of pervasive computing in the current literature. Thus specific inclusion criteria for selecting articles about relevant systems were developed. Searches were conducted in four scientific databases alongside manual journal searches for the period of 2002 to 2006. Articles included present prototypes, case studies and pilot studies, clinical trials and systems that are already in routine use. The searches identified 69 articles describing 67 different systems. In a quantitative analysis, these systems were categorized into project status, health care settings, user groups, improvement aims, and systems features (i.e., component types, data gathering, data transmission, systems functions). The focus is on the types of systems implemented, their frequency of occurrence and their characteristics. Qualitative analyses were performed of deployment issues, such as organizational and personnel issues, privacy and security issues, and financial issues. This paper provides a comprehensive access to the literature of the emerging field by addressing specific topics of application settings, systems features, and deployment experiences. Both an overview and an analysis of the literature on a broad and heterogeneous range of systems are provided. Most systems are described in their prototype stages. Deployment issues, such as implications on organization or personnel, privacy concerns, or financial issues are mentioned rarely, though their solution is regarded as decisive in transferring promising systems to a stage of regular operation. There is a need for further research on the deployment of pervasive computing systems, including clinical studies, economic and social analyses, user studies, etc.

Earth Science Data Fusion with Event Building Approach

NASA Technical Reports Server (NTRS)

Lukashin, C.; Bartle, Ar.; Callaway, E.; Gyurjyan, V.; Mancilla, S.; Oyarzun, R.; Vakhnin, A.

2015-01-01

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

A Privacy-Preserving Platform for User-Centric Quantitative Benchmarking

NASA Astrophysics Data System (ADS)

Herrmann, Dominik; Scheuer, Florian; Feustel, Philipp; Nowey, Thomas; Federrath, Hannes

We propose a centralised platform for quantitative benchmarking of key performance indicators (KPI) among mutually distrustful organisations. Our platform offers users the opportunity to request an ad-hoc benchmarking for a specific KPI within a peer group of their choice. Architecture and protocol are designed to provide anonymity to its users and to hide the sensitive KPI values from other clients and the central server. To this end, we integrate user-centric peer group formation, exchangeable secure multi-party computation protocols, short-lived ephemeral key pairs as pseudonyms, and attribute certificates. We show by empirical evaluation of a prototype that the performance is acceptable for reasonably sized peer groups.

Introduction to the article collection 'Translation in healthcare: ethical, legal, and social implications'.

PubMed

Morrison, Michael; Dickenson, Donna; Lee, Sandra Soo-Jin

2016-11-14

New technologies are transforming and reconfiguring the boundaries between patients, research participants and consumers, between research and clinical practice, and between public and private domains. From personalised medicine to big data and social media, these platforms facilitate new kinds of interactions, challenge longstanding understandings of privacy and consent, and raise fundamental questions about how the translational patient pathway should be organised.This editorial introduces the cross-journal article collection "Translation in healthcare: ethical, legal, and social implications", briefly outlining the genesis of the collection in the 2015 Translation in healthcare conference in Oxford, UK and providing an introduction to the contemporary ethical challenges of translational research in biology and medicine accompanied by a summary of the papers included in this collection.

Development of a gas cell-based laser ion source for RIKEN PALIS

NASA Astrophysics Data System (ADS)

Sonoda, T.; Wada, M.; Tomita, H.; Sakamoto, C.; Takatsuka, T.; Noto, T.; Iimura, H.; Matsuo, Y.; Kubo, T.; Shinozuka, T.; Wakui, T.; Mita, H.; Naimi, S.; Furukawa, T.; Itou, Y.; Schury, P.; Miyatake, H.; Jeong, S.; Ishiyama, H.; Watanabe, Y.; Hirayama, Y.

2013-04-01

We developed a prototype laser ionization gas cell with a beam extraction system. This device is for use of PArasitic Laser Ion-Source (PALIS), which will be implemented into RIKEN's fragment separator, BigRIPS as a part of SLOWRI. Off-line resonant laser ionization for stable Co, Cu, Fe, Ni, Ti, Nb, Sn, In and Pd inside the gas cell, ion extraction and transport to the high-vacuum region via SPIG and QMS have been confirmed (Sonoda et al, Nucl Instrum Meth B 295:1, 2013).

openPDS: protecting the privacy of metadata through SafeAnswers.

PubMed

de Montjoye, Yves-Alexandre; Shmueli, Erez; Wang, Samuel S; Pentland, Alex Sandy

2014-01-01

The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual. This lack of access and control is furthermore fueling growing concerns, as it prevents individuals from understanding and managing the risks associated with the collection and use of their data. Our contribution is two-fold: (1) we describe openPDS, a personal metadata management framework that allows individuals to collect, store, and give fine-grained access to their metadata to third parties. It has been implemented in two field studies; (2) we introduce and analyze SafeAnswers, a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. The dimensionality of the data shared with the services is reduced from high-dimensional metadata to low-dimensional answers that are less likely to be re-identifiable and to contain sensitive information. These answers can then be directly shared individually or in aggregate. openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata, thereby supporting the creation of smart data-driven services and data science research.

openPDS: Protecting the Privacy of Metadata through SafeAnswers

PubMed Central

de Montjoye, Yves-Alexandre; Shmueli, Erez; Wang, Samuel S.; Pentland, Alex Sandy

2014-01-01

The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual. This lack of access and control is furthermore fueling growing concerns, as it prevents individuals from understanding and managing the risks associated with the collection and use of their data. Our contribution is two-fold: (1) we describe openPDS, a personal metadata management framework that allows individuals to collect, store, and give fine-grained access to their metadata to third parties. It has been implemented in two field studies; (2) we introduce and analyze SafeAnswers, a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals' metadata. The dimensionality of the data shared with the services is reduced from high-dimensional metadata to low-dimensional answers that are less likely to be re-identifiable and to contain sensitive information. These answers can then be directly shared individually or in aggregate. openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata, thereby supporting the creation of smart data-driven services and data science research. PMID:25007320

Self-monitoring of driving speed.

PubMed

Etzioni, Shelly; Erev, Ido; Ishaq, Robert; Elias, Wafa; Shiftan, Yoram

2017-09-01

In-vehicle data recorders (IVDR) have been found to facilitate safe driving and are highly valuable in accident analysis. Nevertheless, it is not easy to convince drivers to use them. Part of the difficulty is related to the "Big Brother" concern: installing IVDR impairs the drivers' privacy. The "Big Brother" concern can be mitigated by adding a turn-off switch to the IVDR. However, this addition comes at the expense of increasing speed variability between drivers, which is known to impair safety. The current experimental study examines the significance of this negative effect of a turn-off switch under two experimental settings representing different incentive structures: small and large fines for speeding. 199 students were asked to participate in a computerized speeding dilemma task, where they could control the speed of their "car" using "brake" and "speed" buttons, corresponding to automatic car foot pedals. The participants in two experimental conditions had IVDR installed in their "cars", and were told that they could turn it off at any time. Driving with active IVDR implied some probability of "fines" for speeding, and the two experimental groups differed with respect to the fine's magnitude, small or large. The results indicate that the option to use IVDR reduced speeding and speed variance. In addition, the results indicate that the reduction of speed variability was maximal in the small fine group. These results suggest that using IVDR with gentle fines and with a turn-off option maintains the positive effect of IVDR, addresses the "Big Brother" concern, and does not increase speed variance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developments in clinical trials: a Pharma Matters report.

PubMed

Arjona, A; Nuskey, B; Rabasseda, X; Arias, E

2014-08-01

As the pharmaceutical industry strives to meet the ever-increasing complexity of drug development, new technology in clinical trials has become a beacon of hope. With big data comes the promise of accelerated patient recruitment, real-time monitoring of clinical trials, bioinformatics empowerment of quicker phase progression, and the overwhelming benefits of precision medicine for select trials. Risk-based monitoring stands to benefit as well. With a strengthening focus on centralized data by the FDA and industry's transformative initiative, TransCelerate, a new era in trial risk mitigation has begun. The traditional method of intensive on-site monitoring is becoming a thing of the past as statistical, real-time analysis of site and trial-wide data provides the means to monitor with greater efficiency and effectiveness from afar. However, when it comes to big data, there are challenges that lie ahead. Patient privacy, commercial investment protection, technology woes and data variability are all limitations to be met with considerable thought. At the Annual Meeting of the American Academy of Dermatology this year, clinical trials on psoriasis, atopic dermatitis and other skin diseases were discussed in detail. This review of clinical research reports on novel therapies for psoriasis and atopic dermatitis reveals the impact of these diseases and the drug candidates that have been successful in phase II and III studies. Data-focused highlights of novel dermatological trials, as well as real-life big data approaches and an insight on the new methodology of risk-based monitoring, are all discussed in this edition of Developments in Clinical Trials. Copyright 2014 Prous Science, S.A.U. or its licensors. All rights reserved.

A flexible approach to distributed data anonymization.

PubMed

Kohlmayer, Florian; Prasser, Fabian; Eckert, Claudia; Kuhn, Klaus A

2014-08-01

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets. Copyright © 2013 Elsevier Inc. All rights reserved.

Developing a System for Processing Health Data of Children Using Digitalized Toys: Ethical and Privacy Concerns for the Internet of Things Paradigm.

PubMed

Martín-Ruíz, María Luisa; Fernández-Aller, Celia; Portillo, Eloy; Malagón, Javier; Del Barrio, Cristina

2017-08-16

EDUCERE (Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders) is a government funded research and development project. EDUCERE objectives are to investigate, develop, and evaluate innovative solutions for society to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. In the EDUCERE project, an ethical impact assessment is carried out linked to a minors' data protection rights. Using a specific methodology, the project has achieved some promising results. These include use of a prototype of smart toys to detect development difficulties in children. In addition, privacy protection measures which take into account the security concerns of health data, have been proposed and applied. This latter security framework could be useful in other Internet of Things related projects. It consists of legal and technical measures. Special attention has been placed in the transformation of bulk data such as acceleration and jitter of toys into health data when patterns of atypical development are found. The article describes the different security profiles in which users are classified.

Cloud-based privacy-preserving remote ECG monitoring and surveillance.

PubMed

Page, Alex; Kocabas, Ovunc; Soyata, Tolga; Aktas, Mehmet; Couderc, Jean-Philippe

2015-07-01

The number of technical solutions for monitoring patients in their daily activities is expected to increase significantly in the near future. Blood pressure, heart rate, temperature, BMI, oxygen saturation, and electrolytes are few of the physiologic factors that will soon be available to patients and their physicians almost continuously. The availability and transfer of this information from the patient to the health provider raises privacy concerns. Moreover, current data encryption approaches expose patient data during processing, therefore restricting their utility in applications requiring data analysis. We propose a system that couples health monitoring techniques with analytic methods to permit the extraction of relevant information from patient data without compromising privacy. This proposal is based on the concept of fully homomorphic encryption (FHE). Since this technique is known to be resource-heavy, we develop a proof-of-concept to assess its practicality. Results are presented from our prototype system, which mimics live QT monitoring and detection of drug-induced QT prolongation. Transferring FHE-encrypted QT and RR samples requires about 2 Mbps of network bandwidth per patient. Comparing FHE-encrypted values--for example, comparing QTc to a given threshold-runs quickly enough on modest hardware to alert the doctor of important results in real-time. We demonstrate that FHE could be used to securely transfer and analyze ambulatory health monitoring data. We present a unique concept that could represent a disruptive type of technology with broad applications to multiple monitoring devices. Future work will focus on performance optimizations to accelerate expansion to these other applications. © 2014 Wiley Periodicals, Inc.

Rendering an archive in three dimensions

NASA Astrophysics Data System (ADS)

Leiman, David A.; Twose, Claire; Lee, Teresa Y. H.; Fletcher, Alex; Yoo, Terry S.

2003-05-01

We examine the requirements for a publicly accessible, online collection of three-dimensional biomedical image data, including those yielded by radiological processes such as MRI, ultrasound and others. Intended as a repository and distribution mechanism for such medical data, we created the National Online Volumetric Archive (NOVA) as a case study aimed at identifying the multiple issues involved in realizing a large-scale digital archive. In the paper we discuss such factors as the current legal and health information privacy policy affecting the collection of human medical images, retrieval and management of information and technical implementation. This project culminated in the launching of a website that includes downloadable datasets and a prototype data submission system.

Impact of distributions on the archetypes and prototypes in heterogeneous nanoparticle ensembles.

PubMed

Fernandez, Michael; Wilson, Hugh F; Barnard, Amanda S

2017-01-05

The magnitude and complexity of the structural and functional data available on nanomaterials requires data analytics, statistical analysis and information technology to drive discovery. We demonstrate that multivariate statistical analysis can recognise the sets of truly significant nanostructures and their most relevant properties in heterogeneous ensembles with different probability distributions. The prototypical and archetypal nanostructures of five virtual ensembles of Si quantum dots (SiQDs) with Boltzmann, frequency, normal, Poisson and random distributions are identified using clustering and archetypal analysis, where we find that their diversity is defined by size and shape, regardless of the type of distribution. At the complex hull of the SiQD ensembles, simple configuration archetypes can efficiently describe a large number of SiQDs, whereas more complex shapes are needed to represent the average ordering of the ensembles. This approach provides a route towards the characterisation of computationally intractable virtual nanomaterial spaces, which can convert big data into smart data, and significantly reduce the workload to simulate experimentally relevant virtual samples.

Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services

PubMed Central

Zamani, Hamid

2017-01-01

Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges of data aggregation, maintenance, integration, translation, analysis, and security/privacy. The study objective to establish an interactive BDA platform with simulated patient data using open-source software technologies was achieved by construction of a platform framework with Hadoop Distributed File System (HDFS) using HBase (key-value NoSQL database). Distributed data structures were generated from benchmarked hospital-specific metadata of nine billion patient records. At optimized iteration, HDFS ingestion of HFiles to HBase store files revealed sustained availability over hundreds of iterations; however, to complete MapReduce to HBase required a week (for 10 TB) and a month for three billion (30 TB) indexed patient records, respectively. Found inconsistencies of MapReduce limited the capacity to generate and replicate data efficiently. Apache Spark and Drill showed high performance with high usability for technical support but poor usability for clinical services. Hospital system based on patient-centric data was challenging in using HBase, whereby not all data profiles were fully integrated with the complex patient-to-hospital relationships. However, we recommend using HBase to achieve secured patient data while querying entire hospital volumes in a simplified clinical event model across clinical services. PMID:29375652

Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services.

PubMed

Chrimes, Dillon; Zamani, Hamid

2017-01-01

Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges of data aggregation, maintenance, integration, translation, analysis, and security/privacy. The study objective to establish an interactive BDA platform with simulated patient data using open-source software technologies was achieved by construction of a platform framework with Hadoop Distributed File System (HDFS) using HBase (key-value NoSQL database). Distributed data structures were generated from benchmarked hospital-specific metadata of nine billion patient records. At optimized iteration, HDFS ingestion of HFiles to HBase store files revealed sustained availability over hundreds of iterations; however, to complete MapReduce to HBase required a week (for 10 TB) and a month for three billion (30 TB) indexed patient records, respectively. Found inconsistencies of MapReduce limited the capacity to generate and replicate data efficiently. Apache Spark and Drill showed high performance with high usability for technical support but poor usability for clinical services. Hospital system based on patient-centric data was challenging in using HBase, whereby not all data profiles were fully integrated with the complex patient-to-hospital relationships. However, we recommend using HBase to achieve secured patient data while querying entire hospital volumes in a simplified clinical event model across clinical services.

National Survey of Shiga Toxin-Producing Escherichia coli Serotypes O26, O45, O103, O111, O121, O145, and O157 in Australian Beef Cattle Feces.

PubMed

Mellor, Glen E; Fegan, Narelle; Duffy, Lesley L; McMILLAN, Kate E; Jordan, David; Barlow, Robert S

2016-11-01

Escherichia coli O157 and six non-O157 Shiga toxin-producing E. coli (STEC) serotypes (O26, O45, O103, O111, O121, and O145, colloquially referred to as the "big 6") have been classified as adulterants of raw nonintact beef products in the United States. While beef cattle are a known reservoir for the prototype STEC serotype, E. coli O157, less is known about the dissemination of non-O157 STEC serotypes in Australian cattle. In the present study, 1,500 fecal samples were collected at slaughter from adult (n =628) and young (n =286) beef cattle, adult (n =128) and young (n =143) dairy cattle, and veal calves (n = 315) across 31 Australian export-registered processing establishments. Fecal samples were enriched and tested for E. coli O157 and the big 6 STEC serotypes using BAX System PCR and immunomagnetic separation methods. Pathogenic STEC (pSTEC; isolates that possess stx, eae, and an O antigen marker for O157 or a big 6 serotype) were isolated from 115 samples (7.7%), of which 100 (6.7%) contained E. coli O157 and 19 (1.3%) contained a big 6 serotype. Four of the 115 samples contained multiple pSTEC serotypes. Among samples confirmed for big 6 pSTEC, 15 (1%) contained E. coli O26 and 4 (0.3%) contained E. coli O111. pSTEC of serotypes O45, O103, O121, and O145 were not isolated from any sample, even though genes indicative of E. coli belonging to these serotypes were detected by PCR. Analysis of animal classes revealed a higher pSTEC prevalence in younger animals, including veal (12.7%), young beef (9.8%), and young dairy (7.0%), than in adult animals, including adult beef (5.1%) and adult dairy (3.9%). This study is the largest of its kind undertaken in Australia. In contrast to E. coli O157 and consistent with previous findings, this study reports a relatively low prevalence of big 6 pSTEC serotypes in Australian cattle populations.

Identifying Memory Allocation Patterns in HEP Software

NASA Astrophysics Data System (ADS)

Kama, S.; Rauschmayr, N.

2017-10-01

HEP applications perform an excessive amount of allocations/deallocations within short time intervals which results in memory churn, poor locality and performance degradation. These issues are already known for a decade, but due to the complexity of software frameworks and billions of allocations for a single job, up until recently no efficient mechanism has been available to correlate these issues with source code lines. However, with the advent of the Big Data era, many tools and platforms are now available to do large scale memory profiling. This paper presents, a prototype program developed to track and identify each single (de-)allocation. The CERN IT Hadoop cluster is used to compute memory key metrics, like locality, variation, lifetime and density of allocations. The prototype further provides a web based visualization back-end that allows the user to explore the results generated on the Hadoop cluster. Plotting these metrics for every single allocation over time gives a new insight into application’s memory handling. For instance, it shows which algorithms cause which kind of memory allocation patterns, which function flow causes how many short-lived objects, what are the most commonly allocated sizes etc. The paper will give an insight into the prototype and will show profiling examples for the LHC reconstruction, digitization and simulation jobs.

Guasom Analysis Of The Alhambra Survey

NASA Astrophysics Data System (ADS)

Garabato, Daniel; Manteiga, Minia; Dafonte, Carlos; Álvarez, Marco A.

2017-10-01

GUASOM is a data mining tool designed for knowledge discovery in large astronomical spectrophotometric archives developed in the framework of Gaia DPAC (Data Processing and Analysis Consortium). Our tool is based on a type of unsupervised learning Artificial Neural Networks named Self-organizing maps (SOMs). SOMs permit the grouping and visualization of big amount of data for which there is no a priori knowledge and hence they are very useful for analyzing the huge amount of information present in modern spectrophotometric surveys. SOMs are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized. Each cluster has a representative, called prototype which is a virtual pattern that better represents or resembles the set of input patterns belonging to such a cluster. Prototypes make easier the task of determining the physical nature and properties of the objects populating each cluster. Our algorithm has been tested on the ALHAMBRA survey spectrophotometric observations, here we present our results concerning the survey segmentation, visualization of the data structure, separation between types of objects (stars and galaxies), data homogeneity of neurons, cluster prototypes, redshift distribution and crossmatch with other databases (Simbad).

Towards pervasive computing in health care – A literature review

PubMed Central

Orwat, Carsten; Graefe, Andreas; Faulwasser, Timm

2008-01-01

Background The evolving concepts of pervasive computing, ubiquitous computing and ambient intelligence are increasingly influencing health care and medicine. Summarizing published research, this literature review provides an overview of recent developments and implementations of pervasive computing systems in health care. It also highlights some of the experiences reported in deployment processes. Methods There is no clear definition of pervasive computing in the current literature. Thus specific inclusion criteria for selecting articles about relevant systems were developed. Searches were conducted in four scientific databases alongside manual journal searches for the period of 2002 to 2006. Articles included present prototypes, case studies and pilot studies, clinical trials and systems that are already in routine use. Results The searches identified 69 articles describing 67 different systems. In a quantitative analysis, these systems were categorized into project status, health care settings, user groups, improvement aims, and systems features (i.e., component types, data gathering, data transmission, systems functions). The focus is on the types of systems implemented, their frequency of occurrence and their characteristics. Qualitative analyses were performed of deployment issues, such as organizational and personnel issues, privacy and security issues, and financial issues. This paper provides a comprehensive access to the literature of the emerging field by addressing specific topics of application settings, systems features, and deployment experiences. Conclusion Both an overview and an analysis of the literature on a broad and heterogeneous range of systems are provided. Most systems are described in their prototype stages. Deployment issues, such as implications on organization or personnel, privacy concerns, or financial issues are mentioned rarely, though their solution is regarded as decisive in transferring promising systems to a stage of regular operation. There is a need for further research on the deployment of pervasive computing systems, including clinical studies, economic and social analyses, user studies, etc. PMID:18565221

Online Impact Prioritization of Essential Climate Variables on Climate Change

NASA Astrophysics Data System (ADS)

Forsythe-Newell, S. P.; Barkstrom, B. B.; Roberts, K. P.

2007-12-01

The National Oceanic & Atmospheric Administration (NOAA)'s NCDC Scientific Data Stewardship (SDS) Team has developed an online prototype that is capable of displaying the "big picture" perspective of all Essential Climate Variable (ECV) impacts on society and value to the IPCC. This prototype ECV-Model provides the ability to visualize global ECV information with options to drill down in great detail. It offers a quantifiable prioritization of ECV impacts that potentially may significantly enhance collaboration with respect to dealing effectively with climate change. The ECV-Model prototype assures anonymity and provides an online input mechanism for subject matter experts and decision makers to access, review and submit: (1) ranking of ECV"s, (2) new ECV's and associated impact categories and (3) feedback about ECV"s, satellites, etc. Input and feedback are vetted by experts before changes or additions are implemented online. The SDS prototype also provides an intuitive one-stop web site that displays past, current and planned launches of satellites; and general as well as detailed information in conjunction with imagery. NCDC's version 1.0 release will be available to the public and provide an easy "at-a-glance" interface to rapidly identify gaps and overlaps of satellites and associated instruments monitoring climate change ECV's. The SDS version 1.1 will enhance depiction of gaps and overlaps with instruments associated with In-Situ and Satellites related to ECVs. NOAA's SDS model empowers decision makers and the scientific community to rapidly identify weaknesses and strengths in monitoring climate change ECV's and potentially significantly enhance collaboration.

Data provenance assurance in the cloud using blockchain

NASA Astrophysics Data System (ADS)

Shetty, Sachin; Red, Val; Kamhoua, Charles; Kwiat, Kevin; Njilla, Laurent

2017-05-01

Ever increasing adoption of cloud technology scales up the activities like creation, exchange, and alteration of cloud data objects, which create challenges to track malicious activities and security violations. Addressing this issue requires implementation of data provenance framework so that each data object in the federated cloud environment can be tracked and recorded but cannot be modified. The blockchain technology gives a promising decentralized platform to build tamper-proof systems. Its incorruptible distributed ledger/blockchain complements the need of maintaining cloud data provenance. In this paper, we present a cloud based data provenance framework using block chain which traces data record operations and generates provenance data. We anchor provenance data records into block chain transactions, which provide validation on provenance data and preserve user privacy at the same time. Once the provenance data is uploaded to the global block chain network, it is extremely challenging to tamper the provenance data. Besides, the provenance data uses hashed user identifiers prior to uploading so the blockchain nodes cannot link the operations to a particular user. The framework ensures that the privacy is preserved. We implemented the architecture on ownCloud, uploaded records to blockchain network, stored records in a provenance database and developed a prototype in form of a web service.

The Use of ICT to Support Perpetual Undergraduate Students

NASA Astrophysics Data System (ADS)

Rahayu, N. W.; Huda, S. N.

2017-03-01

Perpetual students are problematic, both for campus and for themselves. Inefficient student management could lead to bad lecturer: students’ ratio and cause complicated problem for students and parents. This paper describes ICT used by an Informatics department of a big private university in Indonesia to help 203 perpetual undergraduate students finishing their study in a short time. Lengths of study are varying from 7 until 15 years and most of them suffered from some compulsory credits and mandatory internship project. We observed wide-range of ICT used for data management and communication during the beginning, middle and the end of periods. Success rate of finding perpetual students and producing graduates are almost 70%, but this percentage could be higher if we maximize ICT use. Information sharing type, social media, privacy and patience become important issues related with the use of ICT.

Use Hierarchical Storage and Analysis to Exploit Intrinsic Parallelism

NASA Astrophysics Data System (ADS)

Zender, C. S.; Wang, W.; Vicente, P.

2013-12-01

Big Data is an ugly name for the scientific opportunities and challenges created by the growing wealth of geoscience data. How to weave large, disparate datasets together to best reveal their underlying properties, to exploit their strengths and minimize their weaknesses, to continually aggregate more information than the world knew yesterday and less than we will learn tomorrow? Data analytics techniques (statistics, data mining, machine learning, etc.) can accelerate pattern recognition and discovery. However, often researchers must, prior to analysis, organize multiple related datasets into a coherent framework. Hierarchical organization permits entire dataset to be stored in nested groups that reflect their intrinsic relationships and similarities. Hierarchical data can be simpler and faster to analyze by coding operators to automatically parallelize processes over isomorphic storage units, i.e., groups. The newest generation of netCDF Operators (NCO) embody this hierarchical approach, while still supporting traditional analysis approaches. We will use NCO to demonstrate the trade-offs involved in processing a prototypical Big Data application (analysis of CMIP5 datasets) using hierarchical and traditional analysis approaches.

Privacy and information security risks in a technology platform for home-based chronic disease rehabilitation and education.

PubMed

Henriksen, Eva; Burkow, Tatjana M; Johnsen, Elin; Vognild, Lars K

2013-08-09

Privacy and information security are important for all healthcare services, including home-based services. We have designed and implemented a prototype technology platform for providing home-based healthcare services. It supports a personal electronic health diary and enables secure and reliable communication and interaction with peers and healthcare personnel. The platform runs on a small computer with a dedicated remote control. It is connected to the patient's TV and to a broadband Internet. The platform has been tested with home-based rehabilitation and education programs for chronic obstructive pulmonary disease and diabetes. As part of our work, a risk assessment of privacy and security aspects has been performed, to reveal actual risks and to ensure adequate information security in this technical platform. Risk assessment was performed in an iterative manner during the development process. Thus, security solutions have been incorporated into the design from an early stage instead of being included as an add-on to a nearly completed system. We have adapted existing risk management methods to our own environment, thus creating our own method. Our method conforms to ISO's standard for information security risk management. A total of approximately 50 threats and possible unwanted incidents were identified and analysed. Among the threats to the four information security aspects: confidentiality, integrity, availability, and quality; confidentiality threats were identified as most serious, with one threat given an unacceptable level of High risk. This is because health-related personal information is regarded as sensitive. Availability threats were analysed as low risk, as the aim of the home programmes is to provide education and rehabilitation services; not for use in acute situations or for continuous health monitoring. Most of the identified threats are applicable for healthcare services intended for patients or citizens in their own homes. Confidentiality risks in home are different from in a more controlled environment such as a hospital; and electronic equipment located in private homes and communicating via Internet, is more exposed to unauthorised access. By implementing the proposed measures, it has been possible to design a home-based service which ensures the necessary level of information security and privacy.

Who Owns the Data? Open Data for Healthcare

PubMed Central

Kostkova, Patty; Brewer, Helen; de Lusignan, Simon; Fottrell, Edward; Goldacre, Ben; Hart, Graham; Koczan, Phil; Knight, Peter; Marsolier, Corinne; McKendry, Rachel A.; Ross, Emma; Sasse, Angela; Sullivan, Ralph; Chaytor, Sarah; Stevenson, Olivia; Velho, Raquel; Tooke, John

2016-01-01

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual’s characteristics, known as personalized or “stratified medicine.” Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become “data donors” and the “quantified self” movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of ­healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals’ NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health. PMID:26925395

Ethical issues in using Twitter for population-level depression monitoring: a qualitative study.

PubMed

Mikal, Jude; Hurst, Samantha; Conway, Mike

2016-04-14

Recently, significant research effort has focused on using Twitter (and other social media) to investigate mental health at the population-level. While there has been influential work in developing ethical guidelines for Internet discussion forum-based research in public health, there is currently limited work focused on addressing ethical problems in Twitter-based public health research, and less still that considers these issues from users' own perspectives. In this work, we aim to investigate public attitudes towards utilizing public domain Twitter data for population-level mental health monitoring using a qualitative methodology. The study explores user perspectives in a series of five, 2-h focus group interviews. Following a semi-structured protocol, 26 Twitter users with and without a diagnosed history of depression discussed general Twitter use, along with privacy expectations, and ethical issues in using social media for health monitoring, with a particular focus on mental health monitoring. Transcripts were then transcribed, redacted, and coded using a constant comparative approach. While participants expressed a wide range of opinions, there was an overall trend towards a relatively positive view of using public domain Twitter data as a resource for population level mental health monitoring, provided that results are appropriately aggregated. Results are divided into five sections: (1) a profile of respondents' Twitter use patterns and use variability; (2) users' privacy expectations, including expectations regarding data reach and permanence; (3) attitudes towards social media based population-level health monitoring in general, and attitudes towards mental health monitoring in particular; (4) attitudes towards individual versus population-level health monitoring; and (5) users' own recommendations for the appropriate regulation of population-level mental health monitoring. Focus group data reveal a wide range of attitudes towards the use of public-domain social media "big data" in population health research, from enthusiasm, through acceptance, to opposition. Study results highlight new perspectives in the discussion of ethical use of public data, particularly with respect to consent, privacy, and oversight.

Who Owns the Data? Open Data for Healthcare.

PubMed

Kostkova, Patty; Brewer, Helen; de Lusignan, Simon; Fottrell, Edward; Goldacre, Ben; Hart, Graham; Koczan, Phil; Knight, Peter; Marsolier, Corinne; McKendry, Rachel A; Ross, Emma; Sasse, Angela; Sullivan, Ralph; Chaytor, Sarah; Stevenson, Olivia; Velho, Raquel; Tooke, John

2016-01-01

Research on large shared medical datasets and data-driven research are gaining fast momentum and provide major opportunities for improving health systems as well as individual care. Such open data can shed light on the causes of disease and effects of treatment, including adverse reactions side-effects of treatments, while also facilitating analyses tailored to an individual's characteristics, known as personalized or "stratified medicine." Developments, such as crowdsourcing, participatory surveillance, and individuals pledging to become "data donors" and the "quantified self" movement (where citizens share data through mobile device-connected technologies), have great potential to contribute to our knowledge of disease, improving diagnostics, and delivery of -healthcare and treatment. There is not only a great potential but also major concerns over privacy, confidentiality, and control of data about individuals once it is shared. Issues, such as user trust, data privacy, transparency over the control of data ownership, and the implications of data analytics for personal privacy with potentially intrusive inferences, are becoming increasingly scrutinized at national and international levels. This can be seen in the recent backlash over the proposed implementation of care.data, which enables individuals' NHS data to be linked, retained, and shared for other uses, such as research and, more controversially, with businesses for commercial exploitation. By way of contrast, through increasing popularity of social media, GPS-enabled mobile apps and tracking/wearable devices, the IT industry and MedTech giants are pursuing new projects without clear public and policy discussion about ownership and responsibility for user-generated data. In the absence of transparent regulation, this paper addresses the opportunities of Big Data in healthcare together with issues of responsibility and accountability. It also aims to pave the way for public policy to support a balanced agenda that safeguards personal information while enabling the use of data to improve public health.

Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources.

PubMed

Huang, Yingxiang; Lee, Junghye; Wang, Shuang; Sun, Jimeng; Liu, Hongfang; Jiang, Xiaoqian

2018-05-16

Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care. ©Yingxiang Huang, Junghye Lee, Shuang Wang, Jimeng Sun, Hongfang Liu, Xiaoqian Jiang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.05.2018.

Biomedical Big Data: New Models of Control Over Access, Use and Governance.

PubMed

Vayena, Effy; Blasimme, Alessandro

2017-12-01

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.

Personality prototype as a risk factor for eating disorders.

PubMed

Sanchez-Guarnido, Antonio J; Pino-Osuna, Maria J; Herruzo-Cabrera, Francisco J

2015-01-01

To establish whether the risk of suffering from an eating disorder (ED) is associated with the high-functioning, undercontrolled, or overcontrolled personality prototype groups. The Revised NEO Personality Inventory (NEO-PI-R) and the Eating Disorder Inventory 2 (EDI-2) were administered to 69 patients diagnosed as suffering from EDs (cases) and 89 people free of any ED symptoms (control group). A cluster analysis was carried out to divide the participants into three groups based on their scores in the Big Five personality dimensions. A logistic regression model was then created. Participants in the undercontrolled group had a risk of suffering from an ED 6.517 times higher than those in the high-functioning group (p = 0.019; odds ratio [OR] = 6.517), while those in the overcontrolled subgroup had a risk of ED 15.972 times higher than those in the high-functioning group. Two personality subtypes were identified in which the risk of EDs was six times higher (the undercontrolled group) and almost 16 times higher (the overcontrolled group). Prevention and treatment programs for ED could benefit from focusing on the abovementioned personality profiles.

The endo-rectal probe prototype for the TOPEM project

NASA Astrophysics Data System (ADS)

Musico, Paolo; TOPEM Collaboration

2016-07-01

The TOPEM project was funded by INFN with the aim of studying the design of a TOF-PET system dedicated to prostate imaging. During last year a big effort was put into building the prototype of the endo-rectal probe from all point of view: mechanical, thermal, electrical. A dedicated integrated circuit was adopted to have the minimum dimensions: the TOFPET ASIC. The system is composed by a LYSO pixellated crystal which is seen by a 128 SiPM matrix on both surfaces: this permits Depth Of Interaction (DOI) measurement. The 4 needed ASICs are handled by a FPGA board which transmits the acquired data over an UDP connection. The external container was made using 3-D printing technology: internal channels on the external surface permit the flowing of controlled temperature (≈35 °C) water. Electronic components power is dissipated using an internal air flow kept at lower temperature (≈20 °C). The probe is MR compatible: a dedicated small antenna can be accommodated in the container. This will permit simultaneous imaging in MRI and PET systems.

Medical cyber-physical systems: A survey.

PubMed

Dey, Nilanjan; Ashour, Amira S; Shi, Fuqian; Fong, Simon James; Tavares, João Manuel R S

2018-03-10

Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

Beyond the corporeal: Extending propertisation of body parts to derivative information.

PubMed

Bonython, Wendy; Arnold, Bruce Baer

2016-03-01

Jurisprudential consideration of property in the human body has typically conceptualised it as tangible, of finite lifespan, with limited end uses. This article offers an alternative conceptualisation: the body as information--intangible, infinite, and perpetual. Global markets in health "big data"--including population genomic data--trade this information. Emerging jurisprudence on source rights in this information are derived from jurisprudence based on the traditional, tangible, finite conceptualisation of the body--itself controversial--criticised in part for disregarding property rights vesting in the self, while recognising them in strangers. As such, it provides an uncertain foundation for extension to govern rights over derivatives, enabling disregard of legitimate concerns about health, commercialisation and genetic privacy, concerns compounded by the intergenerational nature of genetic information. A more nuanced approach, recognising that donors and strangers alike hold only weak custodial rights over access, use, and dissemination of tissues and derivative information, is required.

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing.

PubMed

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-07-24

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient.

SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.

PubMed

Chenghong, Wang; Jiang, Yichen; Mohammed, Noman; Chen, Feng; Jiang, Xiaoqian; Al Aziz, Md Momin; Sadat, Md Nazmus; Wang, Shuang

2017-01-01

As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.

SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach

PubMed Central

Chenghong, Wang; Jiang, Yichen; Mohammed, Noman; Chen, Feng; Jiang, Xiaoqian; Al Aziz, Md Momin; Sadat, Md Nazmus; Wang, Shuang

2017-01-01

As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project. PMID:29854245

Expanding Access to Large-Scale Genomic Data While Promoting Privacy: A Game Theoretic Approach.

PubMed

Wan, Zhiyu; Vorobeychik, Yevgeniy; Xia, Weiyi; Clayton, Ellen Wright; Kantarcioglu, Murat; Malin, Bradley

2017-02-02

Emerging scientific endeavors are creating big data repositories of data from millions of individuals. Sharing data in a privacy-respecting manner could lead to important discoveries, but high-profile demonstrations show that links between de-identified genomic data and named persons can sometimes be reestablished. Such re-identification attacks have focused on worst-case scenarios and spurred the adoption of data-sharing practices that unnecessarily impede research. To mitigate concerns, organizations have traditionally relied upon legal deterrents, like data use agreements, and are considering suppressing or adding noise to genomic variants. In this report, we use a game theoretic lens to develop more effective, quantifiable protections for genomic data sharing. This is a fundamentally different approach because it accounts for adversarial behavior and capabilities and tailors protections to anticipated recipients with reasonable resources, not adversaries with unlimited means. We demonstrate this approach via a new public resource with genomic summary data from over 8,000 individuals-the Sequence and Phenotype Integration Exchange (SPHINX)-and show that risks can be balanced against utility more effectively than with traditional approaches. We further show the generalizability of this framework by applying it to other genomic data collection and sharing endeavors. Recognizing that such models are dependent on a variety of parameters, we perform extensive sensitivity analyses to show that our findings are robust to their fluctuations. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

A survey of Estonian consumer expectations from the pharmacy service and a comparison with the opinions of pharmacists.

PubMed

Villako, Peeter; Raal, Ain

2007-10-01

To assess the preferences of pharmacy customers when choosing a pharmacy and their expectations of the service, and comparing these with the opinions of pharmacists. Opinion and satisfaction of community pharmacy clients in Estonia. A written survey was carried out among pharmacy customers (n=1979) in cities (in 3 community pharmacies), towns (in 2 community pharmacies), and in small towns (in 2 community pharmacies). The survey was also carried out among community pharmacists (n=135) in different regions of Estonia. When choosing a pharmacy, its location was considered most important, costs and wide choice are less important. The most important expectations of customers' included help choosing the right medicine, as well as professional consultation. Preferences and expectations of pharmacy customers depended on their age, gender and income. Parking space, quickness and pleasantness were considered important by men. Costs and wide choice were considered to be more important by women. Pharmacists wish to give patients more information, but they overestimate the importance of quick service. Customers favoured privacy, discretion and confidentiality more. These characteristics were especially important to younger well-paid people living in big cities. In contrast to the opinions offered by pharmacists', a rapid customer service is so not as important as the quality of service in pharmacy according to customers. They also emphasised that pharmacies should highlight the choice of products, quality of service, a professional consultation, as well as ensure privacy.

Deriving Requirements for Pervasive Well-Being Technology From Work Stress and Intervention Theory: Framework and Case Study.

PubMed

Koldijk, Saskia; Kraaij, Wessel; Neerincx, Mark A

2016-07-05

Stress in office environments is a big concern, often leading to burn-out. New technologies are emerging, such as easily available sensors, contextual reasoning, and electronic coaching (e-coaching) apps. In the Smart Reasoning for Well-being at Home and at Work (SWELL) project, we explore the potential of using such new pervasive technologies to provide support for the self-management of well-being, with a focus on individuals' stress-coping. Ideally, these new pervasive systems should be grounded in existing work stress and intervention theory. However, there is a large diversity of theories and they hardly provide explicit directions for technology design. The aim of this paper is to present a comprehensive and concise framework that can be used to design pervasive technologies that support knowledge workers to decrease stress. Based on a literature study we identify concepts relevant to well-being at work and select different work stress models to find causes of work stress that can be addressed. From a technical perspective, we then describe how sensors can be used to infer stress and the context in which it appears, and use intervention theory to further specify interventions that can be provided by means of pervasive technology. The resulting general framework relates several relevant theories: we relate "engagement and burn-out" to "stress", and describe how relevant aspects can be quantified by means of sensors. We also outline underlying causes of work stress and how these can be addressed with interventions, in particular utilizing new technologies integrating behavioral change theory. Based upon this framework we were able to derive requirements for our case study, the pervasive SWELL system, and we implemented two prototypes. Small-scale user studies proved the value of the derived technology-supported interventions. The presented framework can be used to systematically develop theory-based technology-supported interventions to address work stress. In the area of pervasive systems for well-being, we identified the following six key research challenges and opportunities: (1) performing multi-disciplinary research, (2) interpreting personal sensor data, (3) relating measurable aspects to burn-out, (4) combining strengths of human and technology, (5) privacy, and (6) ethics.

Light field reconstruction robust to signal dependent noise

NASA Astrophysics Data System (ADS)

Ren, Kun; Bian, Liheng; Suo, Jinli; Dai, Qionghai

2014-11-01

Capturing four dimensional light field data sequentially using a coded aperture camera is an effective approach but suffers from low signal noise ratio. Although multiplexing can help raise the acquisition quality, noise is still a big issue especially for fast acquisition. To address this problem, this paper proposes a noise robust light field reconstruction method. Firstly, scene dependent noise model is studied and incorporated into the light field reconstruction framework. Then, we derive an optimization algorithm for the final reconstruction. We build a prototype by hacking an off-the-shelf camera for data capturing and prove the concept. The effectiveness of this method is validated with experiments on the real captured data.

Machine Learning for Zwicky Transient Facility

NASA Astrophysics Data System (ADS)

Mahabal, Ashish; Zwicky Transient Facility, Catalina Real-Time Transient Survey

2018-01-01

The Zwicky Transient Facility (ZTF) will operate from 2018 to 2020 covering the accessible sky with its large 47 square degree camera. The transient detection rate is expected to be about a million per night. ZTF is thus a perfect LSST prototype. The big difference is that all of the ZTF transients can be followed up by 4- to 8-m class telescopes. Given the large numbers, using human scanners for separating the genuine transients from artifacts is out of question. For that first step as well as for classifying the transients with minimal follow-up requires machine learning. We describe the tools and plans to take on this task using follow-up facilities, and knowledge gained from archival datasets.

Development Tests of a Cryogenic Filter Wheel Assembly for the NIRCam Instrument

NASA Technical Reports Server (NTRS)

McCully, Sean; Clark, Charles; Schermerhorn, Michael; Trojanek, Filip; O'Hara, Mark; Williams, Jeff; Thatcher, John

2006-01-01

The James Webb Space Telescope is an infrared-optimized space telescope scheduled for launch in 201 3. Its 6.5-m diameter primary mirror will collect light from some of the first galaxies formed after the big bang. The Near Infrared camera (NIRCam) will detect the first light from these galaxies, provide the necessary tools for studying the formation of stars, aid in discovering planets around other stars, and adjust the wave front error on the primary mirror (Fig. 1). The instrument and its complement of mechanisms and optics will operate at a cryogenic temperature of 35 K. This paper describes tests and test results of the NIRCam Filter Wheel assembly prototype.

Prevention of sexually transmitted infections using mobile devices and ubiquitous computing.

PubMed

Besoain, Felipe; Perez-Navarro, Antoni; Caylà, Joan A; Aviñó, Constanza Jacques; de Olalla, Patricia García

2015-05-03

Advances in the development of information and communication technologies have facilitated social interrelationships, but also sexual contacts without appropriate preventive measures. In this paper, we will focus on situations in which people use applications to meet sexual partners nearby, which could increase their chance of exposure to sexually transmitted infections (STI). How can we encourage users to adopt preventive measures without violating their privacy or infringing on the character of the application? To achieve the goal of preventing STI, we have used the design and creation methodology and have developed a prototype software package. This prototype follows the RESTful services principles and has two parts: an Android OS application with emphasis on ubiquitous computing and designed according to General Responsibility Assignment Software Patterns (GRASP), and a server with a web page. To choose the preventive messages, we performed a test in 17 men who have sex with men (MSM). Our software sends preventive notifications to users when it detects situations such as the activation of particular applications on their smartphones, or their proximity to areas with a high probability of intercourse (hot zones). The underlying idea is the same as that for warning messages on cigarette packets, since users read the message just when they are going to smoke. The messages used have been selected from a list that has been rated by the users themselves. The most popular message is "Enjoy sex and enjoy life. Do not expose yourself to HIV". The user is unaware of the software, which runs in the background. Ubiquitous computing may be useful for alerting users with preventive and educational messages. The proposed application is non-intrusive because: 1) the users themselves decide to install it and, therefore, users' privacy rights are preserved; 2) it sends a message that helps users think about taking appropriate preventive measures; and 3) it works in the background without interfering with users unless a trigger situation is detected. Thus, this type of application could become an important tool in the complex task of STI prevention.

Sexual health professionals' evaluations of a prototype computer-based contraceptive planning intervention for adolescents: implications for practice.

PubMed

Brown, K E; Abraham, C; Joshi, P; Wallace, L M

2012-09-01

This paper aims to demonstrate how an online planning intervention to enhance contraceptive and condom use among adolescents was viewed by sexual health professionals. It identifies feedback that has facilitated improvement of the intervention both in terms of potential effectiveness and sustainability in practice. The data illustrate how professionals' feedback can enhance intervention development. Ten practitioners (two male; eight female) representing a range of roles in sexual health education and healthcare were given electronic copies of the prototype intervention. Interviews were conducted to elicit feedback. Transcripts of the interviews were subjected to thematic analysis. Practitioners provided positive feedback about the intervention content, use of on-line media, the validity of planning techniques and the inclusion of males in contraceptive planning. Issues with rapport building, trust, privacy, motivation, and time and resources were raised, however, and the promotion of condom carrying was contentious. Professionals' feedback provided scope for developing the intervention to meet practitioners' concerns, thus enhancing likely feasibility and acceptability in practice. Ways in which particular feedback was generalisable to wider theory-based and online intervention development are explored. Some responses indicated that health practitioners would benefit from training to embed theory-based interventions into sexual health education and healthcare.

The SAMI Galaxy Survey: A prototype data archive for Big Science exploration

NASA Astrophysics Data System (ADS)

Konstantopoulos, I. S.; Green, A. W.; Foster, C.; Scott, N.; Allen, J. T.; Fogarty, L. M. R.; Lorente, N. P. F.; Sweet, S. M.; Hopkins, A. M.; Bland-Hawthorn, J.; Bryant, J. J.; Croom, S. M.; Goodwin, M.; Lawrence, J. S.; Owers, M. S.; Richards, S. N.

2015-11-01

We describe the data archive and database for the SAMI Galaxy Survey, an ongoing observational program that will cover ≈3400 galaxies with integral-field (spatially-resolved) spectroscopy. Amounting to some three million spectra, this is the largest sample of its kind to date. The data archive and built-in query engine use the versatile Hierarchical Data Format (HDF5), which precludes the need for external metadata tables and hence the setup and maintenance overhead those carry. The code produces simple outputs that can easily be translated to plots and tables, and the combination of these tools makes for a light system that can handle heavy data. This article acts as a contextual companion to the SAMI Survey Database source code repository, samiDB, which is freely available online and written entirely in Python. We also discuss the decisions related to the selection of tools and the creation of data visualisation modules. It is our aim that the work presented in this article-descriptions, rationale, and source code-will be of use to scientists looking to set up a maintenance-light data archive for a Big Science data load.

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

PubMed

Swan, Melanie

2013-06-01

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.

a New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

NASA Astrophysics Data System (ADS)

Olasz, A.; Nguyen Thai, B.; Kristóf, D.

2016-06-01

Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (https://github.com/posseidon/IQLib/) developed in the frame of the IQmulus EU FP7 research and development project (http://www.iqmulus.eu). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

Privacy and information security risks in a technology platform for home-based chronic disease rehabilitation and education

PubMed Central

2013-01-01

Background Privacy and information security are important for all healthcare services, including home-based services. We have designed and implemented a prototype technology platform for providing home-based healthcare services. It supports a personal electronic health diary and enables secure and reliable communication and interaction with peers and healthcare personnel. The platform runs on a small computer with a dedicated remote control. It is connected to the patient’s TV and to a broadband Internet. The platform has been tested with home-based rehabilitation and education programs for chronic obstructive pulmonary disease and diabetes. As part of our work, a risk assessment of privacy and security aspects has been performed, to reveal actual risks and to ensure adequate information security in this technical platform. Methods Risk assessment was performed in an iterative manner during the development process. Thus, security solutions have been incorporated into the design from an early stage instead of being included as an add-on to a nearly completed system. We have adapted existing risk management methods to our own environment, thus creating our own method. Our method conforms to ISO’s standard for information security risk management. Results A total of approximately 50 threats and possible unwanted incidents were identified and analysed. Among the threats to the four information security aspects: confidentiality, integrity, availability, and quality; confidentiality threats were identified as most serious, with one threat given an unacceptable level of High risk. This is because health-related personal information is regarded as sensitive. Availability threats were analysed as low risk, as the aim of the home programmes is to provide education and rehabilitation services; not for use in acute situations or for continuous health monitoring. Conclusions Most of the identified threats are applicable for healthcare services intended for patients or citizens in their own homes. Confidentiality risks in home are different from in a more controlled environment such as a hospital; and electronic equipment located in private homes and communicating via Internet, is more exposed to unauthorised access. By implementing the proposed measures, it has been possible to design a home-based service which ensures the necessary level of information security and privacy. PMID:23937965

The effects of overall robot shape on the emotions invoked in users and the perceived personalities of robot.

PubMed

Hwang, Jihong; Park, Taezoon; Hwang, Wonil

2013-05-01

The affective interaction between human and robots could be influenced by various aspects of robots, which are appearance, countenance, gesture, voice, etc. Among these, the overall shape of robot could play a key role in invoking desired emotions to the users and bestowing preferred personalities to robots. In this regard, the present study experimentally investigates the effects of overall robot shape on the emotions invoked in users and the perceived personalities of robot with an objective of deriving guidelines for the affective design of service robots. In so doing, 27 different shapes of robot were selected, modeled and fabricated, which were combinations of three different shapes of head, trunk and limb (legs and arms) - rectangular-parallelepiped, cylindrical and human-like shapes. For the experiment, visual images and real prototypes of these robot shapes were presented to participants, and emotions invoked and personalities perceived from the presented robots were measured. The results showed that the overall shape of robot arouses any of three emotions named 'concerned', 'enjoyable' and 'favorable', among which 'concerned' emotion is negatively correlated with the 'big five personality factors' while 'enjoyable' and 'favorable' emotions are positively correlated. It was found that the 'big five personality factors', and 'enjoyable' and 'favorable' emotions are more strongly perceived through the real prototypes than through the visual images. It was also found that the robot shape consisting of cylindrical head, human-like trunk and cylindrical head is the best for 'conscientious' personality and 'favorable' emotion, the robot shape consisting of cylindrical head, human-like trunk and human-like limb for 'extroverted' personality, the robot shape consisting of cylindrical head, cylindrical trunk and cylindrical limb for 'anti-neurotic' personality, and the robot shape consisting of rectangular-parallelepiped head, human-like trunk and human-like limb for 'enjoyable' emotion. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Developing a Security Metrics Scorecard for Healthcare Organizations.

PubMed

Elrefaey, Heba; Borycki, Elizabeth; Kushniruk, Andrea

2015-01-01

In healthcare, information security is a key aspect of protecting a patient's privacy and ensuring systems availability to support patient care. Security managers need to measure the performance of security systems and this can be achieved by using evidence-based metrics. In this paper, we describe the development of an evidence-based security metrics scorecard specific to healthcare organizations. Study participants were asked to comment on the usability and usefulness of a prototype of a security metrics scorecard that was developed based on current research in the area of general security metrics. Study findings revealed that scorecards need to be customized for the healthcare setting in order for the security information to be useful and usable in healthcare organizations. The study findings resulted in the development of a security metrics scorecard that matches the healthcare security experts' information requirements.

Manufacturing data analytics using a virtual factory representation.

PubMed

Jain, Sanjay; Shao, Guodong; Shin, Seung-Jun

2017-01-01

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Privacy and Technology: Folk Definitions and Perspectives

PubMed Central

Kwasny, Michelle N.; Caine, Kelly E.; Rogers, Wendy A.; Fisk, Arthur D.

2017-01-01

In this paper we present preliminary results from a study of individual differences in privacy beliefs, as well as relate folk definitions of privacy to extant privacy theory. Focus groups were conducted with young adults aged 18–28 and older adults aged 65–75. Participants first shared their individual definitions of privacy, followed by a discussion of privacy in six scenarios chosen to represent a range of potentially invasive situations. Taken together, Westin’s and Altman’s theories of privacy accounted for both younger and older adults’ ideas about privacy, however, neither theory successfully accounted for findings across all age and gender groups. Whereas males tended to think of privacy in terms of personal needs and convenience, females focused more on privacy in terms of others, respecting privacy rights, and safety. Older adults tended to be more concerned about privacy of space rather than information privacy. Initial results reinforce the notion that targeting HCI design to the user population, even with respect to privacy, is critically important. PMID:29057397

From control to causation: Validating a 'complex systems model' of running-related injury development and prevention.

PubMed

Hulme, A; Salmon, P M; Nielsen, R O; Read, G J M; Finch, C F

2017-11-01

There is a need for an ecological and complex systems approach for better understanding the development and prevention of running-related injury (RRI). In a previous article, we proposed a prototype model of the Australian recreational distance running system which was based on the Systems Theoretic Accident Mapping and Processes (STAMP) method. That model included the influence of political, organisational, managerial, and sociocultural determinants alongside individual-level factors in relation to RRI development. The purpose of this study was to validate that prototype model by drawing on the expertise of both systems thinking and distance running experts. This study used a modified Delphi technique involving a series of online surveys (December 2016- March 2017). The initial survey was divided into four sections containing a total of seven questions pertaining to different features associated with the prototype model. Consensus in opinion about the validity of the prototype model was reached when the number of experts who agreed or disagreed with survey statement was ≥75% of the total number of respondents. A total of two Delphi rounds was needed to validate the prototype model. Out of a total of 51 experts who were initially contacted, 50.9% (n = 26) completed the first round of the Delphi, and 92.3% (n = 24) of those in the first round participated in the second. Most of the 24 full participants considered themselves to be a running expert (66.7%), and approximately a third indicated their expertise as a systems thinker (33.3%). After the second round, 91.7% of the experts agreed that the prototype model was a valid description of the Australian distance running system. This is the first study to formally examine the development and prevention of RRI from an ecological and complex systems perspective. The validated model of the Australian distance running system facilitates theoretical advancement in terms of identifying practical system-wide opportunities for the implementation of sustainable RRI prevention interventions. This 'big picture' perspective represents the first step required when thinking about the range of contributory causal factors that affect other system elements, as well as runners' behaviours in relation to RRI risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Generic Privacy Quantification Framework for Privacy-Preserving Data Publishing

ERIC Educational Resources Information Center

Zhu, Zutao

2010-01-01

In recent years, the concerns about the privacy for the electronic data collected by government agencies, organizations, and industries are increasing. They include individual privacy and knowledge privacy. Privacy-preserving data publishing is a research branch that preserves the privacy while, at the same time, withholding useful information in…

A Game Theoretic Framework for Analyzing Re-Identification Risk

PubMed Central

Wan, Zhiyu; Vorobeychik, Yevgeniy; Xia, Weiyi; Clayton, Ellen Wright; Kantarcioglu, Murat; Ganta, Ranjit; Heatherly, Raymond; Malin, Bradley A.

2015-01-01

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing. PMID:25807380

Autonomous Infrastructure for Observatory Operations

NASA Astrophysics Data System (ADS)

Seaman, R.

This is an era of rapid change from ancient human-mediated modes of astronomical practice to a vision of ever larger time domain surveys, ever bigger "big data", to increasing numbers of robotic telescopes and astronomical automation on every mountaintop. Over the past decades, facets of a new autonomous astronomical toolkit have been prototyped and deployed in support of numerous space missions. Remote and queue observing modes have gained significant market share on the ground. Archives and data-mining are becoming ubiquitous; astroinformatic techniques and virtual observatory standards and protocols are areas of active development. Astronomers and engineers, planetary and solar scientists, and researchers from communities as diverse as particle physics and exobiology are collaborating on a vast range of "multi-messenger" science. What then is missing?

Development and Initial Validation of the Multicultural Personality Inventory (MPI).

PubMed

Ponterotto, Joseph G; Fietzer, Alexander W; Fingerhut, Esther C; Woerner, Scott; Stack, Lauren; Magaldi-Dopman, Danielle; Rust, Jonathan; Nakao, Gen; Tsai, Yu-Ting; Black, Natasha; Alba, Renaldo; Desai, Miraj; Frazier, Chantel; LaRue, Alyse; Liao, Pei-Wen

2014-01-01

Two studies summarize the development and initial validation of the Multicultural Personality Inventory (MPI). In Study 1, the 115-item prototype MPI was administered to 415 university students where exploratory factor analysis resulted in a 70-item, 7-factor model. In Study 2, the 70-item MPI and theoretically related companion instruments were administered to a multisite sample of 576 university students. Confirmatory factory analysis found the 7-factor structure to be a relatively good fit to the data (Comparative Fit Index =.954; root mean square error of approximation =.057), and MPI factors predicted variance in criterion variables above and beyond the variance accounted for by broad personality traits (i.e., Big Five). Study limitations and directions for further validation research are specified.

Synthetic ALSPAC longitudinal datasets for the Big Data VR project.

PubMed

Avraam, Demetris; Wilson, Rebecca C; Burton, Paul

2017-01-01

Three synthetic datasets - of observation size 15,000, 155,000 and 1,555,000 participants, respectively - were created by simulating eleven cardiac and anthropometric variables from nine collection ages of the ALSAPC birth cohort study. The synthetic datasets retain similar data properties to the ALSPAC study data they are simulated from (co-variance matrices, as well as the mean and variance values of the variables) without including the original data itself or disclosing participant information.  In this instance, the three synthetic datasets have been utilised in an academia-industry collaboration to build a prototype virtual reality data analysis software, but they could have a broader use in method and software development projects where sensitive data cannot be freely shared.

From Data Privacy to Location Privacy

NASA Astrophysics Data System (ADS)

Wang, Ting; Liu, Ling

Over the past decade, the research on data privacy has achieved considerable advancement in the following two aspects: First, a variety of privacy threat models and privacy principles have been proposed, aiming at providing sufficient protection against different types of inference attacks; Second, a plethora of algorithms and methods have been developed to implement the proposed privacy principles, while attempting to optimize the utility of the resulting data. The first part of the chapter presents an overview of data privacy research by taking a close examination at the achievements from the above two aspects, with the objective of pinpointing individual research efforts on the grand map of data privacy protection. As a special form of data privacy, location privacy possesses its unique characteristics. In the second part of the chapter, we examine the research challenges and opportunities of location privacy protection, in a perspective analogous to data privacy. Our discussion attempts to answer the following three questions: (1) Is it sufficient to apply the data privacy models and algorithms developed to date for protecting location privacy? (2) What is the current state of the research on location privacy? (3) What are the open issues and technical challenges that demand further investigation? Through answering these questions, we intend to provide a comprehensive review of the state of the art in location privacy research.

Choose Privacy Week: Educate Your Students (and Yourself) about Privacy

ERIC Educational Resources Information Center

Adams, Helen R.

2016-01-01

The purpose of "Choose Privacy Week" is to encourage a national conversation to raise awareness of the growing threats to personal privacy online and in day-to-day life. The 2016 Choose Privacy Week theme is "respecting individuals' privacy," with an emphasis on minors' privacy. A plethora of issues relating to minors' privacy…

76 FR 64115 - Privacy Act of 1974; Privacy Act System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-17

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice (11-092)] Privacy Act of 1974; Privacy Act... retirement of one Privacy Act system of records notice. SUMMARY: In accordance with the Privacy Act of 1974, NASA is giving notice that it proposes to cancel the following Privacy Act system of records notice...

Partitioning-based mechanisms under personalized differential privacy.

PubMed

Li, Haoran; Xiong, Li; Ji, Zhanglong; Jiang, Xiaoqian

2017-05-01

Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t -round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.

Partitioning-based mechanisms under personalized differential privacy

PubMed Central

Li, Haoran; Xiong, Li; Ji, Zhanglong; Jiang, Xiaoqian

2017-01-01

Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms. PMID:28932827

Altered states: state health privacy laws and the impact of the Federal Health Privacy Rule.

PubMed

Pritts, Joy L

2002-01-01

Although the Federal Health Privacy Rule has evened out some of the inconsistencies between states' health privacy laws, gaps in protection still remain. Furthermore, the Federal Rule contains some lax standards for the disclosure of health information. State laws can play a vital role in filling these gaps and strengthening the protections afforded health information. By enacting legislation that has higher privacy-protective standards than the Federal Health Privacy Rule, states can play three important roles. First, because they can directly regulate entities that are beyond HHS's mandate, states can afford their citizens a broader degree of privacy protection than the Federal Health Privacy Rule. Second, by having state health privacy laws, states can enforce privacy protections at the local level. Finally, action by the states can positively influence health privacy policies at the federal level by raising the standard as to what constitutes sufficient privacy protection. High privacy protections imposed by states may serve as the standard for comprehensive federal legislation, if and when Congress reconsiders the issue. So far, states' reactions to the Federal Privacy Rule have been mixed. Only time will tell whether states will assume the mantle of leadership on health privacy or relinquish their role as the primary protectors of health information.

Through Patients' Eyes: Regulation, Technology, Privacy, and the Future.

PubMed

Petersen, Carolyn

2018-04-22

Privacy is commonly regarded as a regulatory requirement achieved via technical and organizational management practices. Those working in the field of informatics often play a role in privacy preservation as a result of their expertise in information technology, workflow analysis, implementation science, or related skills. Viewing privacy from the perspective of patients whose protected health information is at risk broadens the considerations to include the perceived duality of privacy; the existence of privacy within a context unique to each patient; the competing needs inherent within privacy management; the need for particular consideration when data are shared; and the need for patients to control health information in a global setting. With precision medicine, artificial intelligence, and other treatment innovations on the horizon, health care professionals need to think more broadly about how to preserve privacy in a health care environment driven by data sharing. Patient-reported privacy preferences, privacy portability, and greater transparency around privacy-preserving functionalities are potential strategies for ensuring that privacy regulations are met and privacy is preserved. Georg Thieme Verlag KG Stuttgart.

[The Comparison of the Difference Between the Emphasis on and the Receipt of Patient Privacy in the Hospital Setting].

PubMed

Li, Chiu-Kuel; Lin, Chiou-Fen

2015-10-01

Privacy is a unique privilege for humans. Enhancing the balance between the importance given to patient privacy and the receipt of this privacy by patients is one key approach to improving the relationship between patients and the hospital. This study compared the difference between the importance of patient privacy and receipt of this privacy by patients as a reference for future patient privacy policy planning. This study was a cross-sectional design. We randomly sampled three hospitals in northern Taiwan and investigated patients using a questionnaire. The questionnaire was self-designed and verified for reliability and validity. We used frequency and percentage to describe demographic data and used a t-test to compare the deviation between the emphasis on and receipt of patient privacy. There were 296 valid samples in this study and the effective rate was 84.57%. The highest degree of importance was information privacy and the lowest was physical privacy. Physical privacy (t = 3.04, p < .001) and mental privacy (t = 2.42, p < .01) exhibited significant differences between emphasis and receipt. Demographic data on gender, age, education level, marital status, and occupation. Type of hospital and ward level correlated with the emphasis and receipt of privacy. The importance of privacy for physical and mental wellbeing was higher than the actual receipt of this privacy among the patient sample. The importance of privacy for married individuals and young women with high education levels was higher, while males and less-educated individuals scored lower in terms of privacy receipt. Medical agencies must provide higher levels of physical and mental privacy in order to enhance patient satisfaction.

Informational privacy and the public's health: the Model State Public Health Privacy Act.

PubMed

Gostin, L O; Hodge, J G; Valdiserri, R O

2001-09-01

Protecting public health requires the acquisition, use, and storage of extensive health-related information about individuals. The electronic accumulation and exchange of personal data promises significant public health benefits but also threatens individual privacy; breaches of privacy can lead to individual discrimination in employment, insurance, and government programs. Individuals concerned about privacy invasions may avoid clinical or public health tests, treatments, or research. Although individual privacy protections are critical, comprehensive federal privacy protections do not adequately protect public health data, and existing state privacy laws are inconsistent and fragmented. The Model State Public Health Privacy Act provides strong privacy safeguards for public health data while preserving the ability of state and local public health departments to act for the common good.

Predicting user concerns about online privacy in Hong Kong.

PubMed

Yao, Mike Z; Zhang, Jinguang

2008-12-01

Empirical studies on people's online privacy concerns have largely been conducted in the West. The global threat of privacy violations on the Internet calls for similar studies to be done in non-Western regions. To fill this void, the current study develops a path model to investigate the influence of people's Internet use-related factors, their beliefs in the right to privacy, and psychological need for privacy on Hong Kong people's concerns about online privacy. Survey responses from 332 university students were analyzed. Results from this study show that people's belief in the right to privacy was the most important predictor of their online privacy concerns. It also significantly mediated the relationship between people's psychological need for privacy and their concerns with privacy violations online. Moreover, while frequent use of the Internet may increase concerns about online privacy issues, Internet use diversity may actually reduce such worries. The final model, well supported by the observed data, successfully explained 25% of the variability in user concerns about online privacy.

Model My Watershed and BiG CZ Data Portal: Interactive geospatial analysis and hydrological modeling web applications that leverage the Amazon cloud for scientists, resource managers and students

NASA Astrophysics Data System (ADS)

Aufdenkampe, A. K.; Mayorga, E.; Tarboton, D. G.; Sazib, N. S.; Horsburgh, J. S.; Cheetham, R.

2016-12-01

The Model My Watershed Web app (http://wikiwatershed.org/model/) was designed to enable citizens, conservation practitioners, municipal decision-makers, educators, and students to interactively select any area of interest anywhere in the continental USA to: (1) analyze real land use and soil data for that area; (2) model stormwater runoff and water-quality outcomes; and (3) compare how different conservation or development scenarios could modify runoff and water quality. The BiG CZ Data Portal is a web application for scientists for intuitive, high-performance map-based discovery, visualization, access and publication of diverse earth and environmental science data via a map-based interface that simultaneously performs geospatial analysis of selected GIS and satellite raster data for a selected area of interest. The two web applications share a common codebase (https://github.com/WikiWatershed and https://github.com/big-cz), high performance geospatial analysis engine (http://geotrellis.io/ and https://github.com/geotrellis) and deployment on the Amazon Web Services (AWS) cloud cyberinfrastructure. Users can use "on-the-fly" rapid watershed delineation over the national elevation model to select their watershed or catchment of interest. The two web applications also share the goal of enabling the scientists, resource managers and students alike to share data, analyses and model results. We will present these functioning web applications and their potential to substantially lower the bar for studying and understanding our water resources. We will also present work in progress, including a prototype system for enabling citizen-scientists to register open-source sensor stations (http://envirodiy.org/mayfly/) to stream data into these systems, so that they can be reshared using Water One Flow web services.

A General-purpose Framework for Parallel Processing of Large-scale LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Z.; Hodgson, M.; Li, W.

2016-12-01

Light detection and ranging (LiDAR) technologies have proven efficiency to quickly obtain very detailed Earth surface data for a large spatial extent. Such data is important for scientific discoveries such as Earth and ecological sciences and natural disasters and environmental applications. However, handling LiDAR data poses grand geoprocessing challenges due to data intensity and computational intensity. Previous studies received notable success on parallel processing of LiDAR data to these challenges. However, these studies either relied on high performance computers and specialized hardware (GPUs) or focused mostly on finding customized solutions for some specific algorithms. We developed a general-purpose scalable framework coupled with sophisticated data decomposition and parallelization strategy to efficiently handle big LiDAR data. Specifically, 1) a tile-based spatial index is proposed to manage big LiDAR data in the scalable and fault-tolerable Hadoop distributed file system, 2) two spatial decomposition techniques are developed to enable efficient parallelization of different types of LiDAR processing tasks, and 3) by coupling existing LiDAR processing tools with Hadoop, this framework is able to conduct a variety of LiDAR data processing tasks in parallel in a highly scalable distributed computing environment. The performance and scalability of the framework is evaluated with a series of experiments conducted on a real LiDAR dataset using a proof-of-concept prototype system. The results show that the proposed framework 1) is able to handle massive LiDAR data more efficiently than standalone tools; and 2) provides almost linear scalability in terms of either increased workload (data volume) or increased computing nodes with both spatial decomposition strategies. We believe that the proposed framework provides valuable references on developing a collaborative cyberinfrastructure for processing big earth science data in a highly scalable environment.

Massively parallel sequencing and the emergence of forensic genomics: Defining the policy and legal issues for law enforcement.

PubMed

Scudder, Nathan; McNevin, Dennis; Kelty, Sally F; Walsh, Simon J; Robertson, James

2018-03-01

Use of DNA in forensic science will be significantly influenced by new technology in coming years. Massively parallel sequencing and forensic genomics will hasten the broadening of forensic DNA analysis beyond short tandem repeats for identity towards a wider array of genetic markers, in applications as diverse as predictive phenotyping, ancestry assignment, and full mitochondrial genome analysis. With these new applications come a range of legal and policy implications, as forensic science touches on areas as diverse as 'big data', privacy and protected health information. Although these applications have the potential to make a more immediate and decisive forensic intelligence contribution to criminal investigations, they raise policy issues that will require detailed consideration if this potential is to be realised. The purpose of this paper is to identify the scope of the issues that will confront forensic and user communities. Copyright © 2017 The Chartered Society of Forensic Sciences. All rights reserved.

Applied Epidemiology and Public Health: Are We Training the Future Generations Appropriately?

PubMed Central

Brownson, Ross C.; Samet, Jonathan M.; Bensyl, Diana M.

2017-01-01

To extend the reach and relevance of epidemiology for public health practice, the science needs be broadened beyond etiologic research, to link more strongly with emerging technologies and to acknowledge key societal transformations. This new focus for epidemiology and its implications for epidemiologic training can be considered in the context of macro trends affecting society, including a greater focus on upstream causes of disease, shifting demographics, the Affordable Care Act and health care system reform, globalization, changing health communication environment, growing centrality of team and transdisciplinary science, emergence of translational sciences, greater focus on accountability, big data, informatics, high-throughput technologies (“omics”), privacy changes, and the evolving funding environment. This commentary describes existing approaches to and competencies for training in epidemiology, maps macro trends with competencies, highlights an example of competency-based education in the Epidemic Intelligence Service of Centers for Disease Control and Prevention, and suggests expanded and more dynamic training approaches. A re-examination of current approaches to epidemiologic training is needed. PMID:28038933

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing

PubMed Central

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-01-01

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient. PMID:28737733

Board on Research Data and Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sztein, A. Ester; Boright, John

2015-08-14

The Board on Research Data and Information (BRDI) has planned and undertaken numerous activities regarding data citation, attribution, management, policy, publishing, centers, access, curation, sharing, and infrastructure; and international collaboration and cooperation. Some of these activities resulted in National Research Council reports (For Attribution: Developing Data Attribution and Citation Practices and Standards (2012), The Case for International Scientific Data Sharing: A Focus on Developing Countries (2012), and The Future of Scientific Knowledge Discovery in Open Networked Environments (2012); and a peer-reviewed paper (Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation ofmore » Data, 2013). BRDI held symposia, workshops and sessions in the U.S. and abroad on diverse topics such as global scientific data infrastructures, discovery of data online, privacy in a big data world, and data citation principles, among other timely data-related subjects. In addition, BRDI effects the representation of the United States before the International Council for Science’s International Committee on Data for Science and Technology (CODATA).« less

Applied epidemiology and public health: are we training the future generations appropriately?

PubMed

Brownson, Ross C; Samet, Jonathan M; Bensyl, Diana M

2017-02-01

To extend the reach and relevance of epidemiology for public health practice, the science needs be broadened beyond etiologic research, to link more strongly with emerging technologies and to acknowledge key societal transformations. This new focus for epidemiology and its implications for epidemiologic training can be considered in the context of macro trends affecting society, including a greater focus on upstream causes of disease, shifting demographics, the Affordable Care Act and health care system reform, globalization, changing health communication environment, growing centrality of team and transdisciplinary science, emergence of translational sciences, greater focus on accountability, big data, informatics, high-throughput technologies ("omics"), privacy changes, and the evolving funding environment. This commentary describes existing approaches to and competencies for training in epidemiology, maps macro trends with competencies, highlights an example of competency-based education in the Epidemic Intelligence Service of Centers for Disease Control and Prevention, and suggests expanded and more dynamic training approaches. A reexamination of current approaches to epidemiologic training is needed. Copyright © 2016 Elsevier Inc. All rights reserved.

Secure and Trustable Electronic Medical Records Sharing using Blockchain.

PubMed

Dubovitskaya, Alevtina; Xu, Zhigang; Ryu, Samuel; Schumacher, Michael; Wang, Fusheng

2017-01-01

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall cost.

Secure and Trustable Electronic Medical Records Sharing using Blockchain

PubMed Central

Dubovitskaya, Alevtina; Xu, Zhigang; Ryu, Samuel; Schumacher, Michael; Wang, Fusheng

2017-01-01

Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall cost. PMID:29854130

Secure Publish-Subscribe Protocols for Heterogeneous Medical Wireless Body Area Networks

PubMed Central

Picazo-Sanchez, Pablo; Tapiador, Juan E.; Peris-Lopez, Pedro; Suarez-Tangil, Guillermo

2014-01-01

Security and privacy issues in medical wireless body area networks (WBANs) constitute a major unsolved concern because of the challenges posed by the scarcity of resources in WBAN devices and the usability restrictions imposed by the healthcare domain. In this paper, we describe a WBAN architecture based on the well-known publish-subscribe paradigm. We present two protocols for publishing data and sending commands to a sensor that guarantee confidentiality and fine-grained access control. Both protocols are based on a recently proposed ciphertext policy attribute-based encryption (CP-ABE) scheme that is lightweight enough to be embedded into wearable sensors. We show how sensors can implement lattice-based access control (LBAC) policies using this scheme, which are highly appropriate for the eHealth domain. We report experimental results with a prototype implementation demonstrating the suitability of our proposed solution. PMID:25460814

Telescience in the Space Station era

NASA Technical Reports Server (NTRS)

Schmerling, E. R.

1988-01-01

Telescience refers to the development of systems where participants involved in research in space can access their fellow scientists and the appropriate NASA services before flight, during flight, and after flight, preferably from their home institutions and through the same equipment. Telescience requires integration of available technologies to develop computer environments that maintain interoperability across different disciplines and different portions of the lifetimes of space experiments, called teledesign, teleoperations, and teleanalysis. Participants in the NASA Telescience Testbed Program are using a rigid prototyping approach to evaluate the necessary technologies and select the options and tradeoffs that best suit their accustomed modalities. The concept of transaction management is described, where the emphasis is placed on the effects of commands, whether event-generated onboard the spacecraft or sent up from the ground. Interoperability, security, and privacy issues are also discussed, and the Telescience Testbed Pilot Program is described.

Limited privacy protection and poor sensitivity: Is it time to move on from the statistical linkage key-581?

PubMed

Randall, Sean M; Ferrante, Anna M; Boyd, James H; Brown, Adrian P; Semmens, James B

2016-08-01

The statistical linkage key (SLK-581) is a common tool for record linkage in Australia, due to its ability to provide some privacy protection. However, newer privacy-preserving approaches may provide greater privacy protection, while allowing high-quality linkage. To evaluate the standard SLK-581, encrypted SLK-581 and a newer privacy-preserving approach using Bloom filters, in terms of both privacy and linkage quality. Linkage quality was compared by conducting linkages on Australian health datasets using these three techniques and examining results. Privacy was compared qualitatively in relation to a series of scenarios where privacy breaches may occur. The Bloom filter technique offered greater privacy protection and linkage quality compared to the SLK-based method commonly used in Australia. The adoption of new privacy-preserving methods would allow both greater confidence in research results, while significantly improving privacy protection. © The Author(s) 2016.

Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy.

PubMed

Mulligan, Deirdre K; Koopman, Colin; Doty, Nick

2016-12-28

The meaning of privacy has been much disputed throughout its history in response to wave after wave of new technological capabilities and social configurations. The current round of disputes over privacy fuelled by data science has been a cause of despair for many commentators and a death knell for privacy itself for others. We argue that privacy's disputes are neither an accidental feature of the concept nor a lamentable condition of its applicability. Privacy is essentially contested. Because it is, privacy is transformable according to changing technological and social conditions. To make productive use of privacy's essential contestability, we argue for a new approach to privacy research and practical design, focused on the development of conceptual analytics that facilitate dissecting privacy's multiple uses across multiple contexts.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

77 FR 70796 - Privacy Act of 1974; Retirement of Department of Homeland Security Transportation Security...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-27

... privacy issues, please contact: Jonathan Cantor, (202-343-1717), Acting Chief Privacy Officer, Privacy... DEPARTMENT OF HOMELAND SECURITY Office of the Secretary Privacy Act of 1974; Retirement of Department of Homeland Security Transportation Security Administration System of Records AGENCY: Privacy...

Privacy and Data Protection in Japan.

ERIC Educational Resources Information Center

Srinivasan, Srinija

1992-01-01

Discussion of individual rights and privacy in Japan focuses on the Privacy Protection Act, which acknowledges the threat posed by government databases to the individual's right of privacy. Characteristics of the Japanese legal system are described, origins of privacy in Japanese law are examined, and privacy and government databases are…

76 FR 19107 - Privacy Act of 1974; Department of Homeland Security Federal Emergency Management Agency-011...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-06

... Ellen Callahan, Chief Privacy Officer, Privacy Office, Department of Homeland Security, Washington, DC... (703-235- 0780), Chief Privacy Officer, Privacy Office, Department of Homeland Security, Washington, DC... Chief Privacy Officer and Chief Freedom of Information Act Officer, Department of Homeland Security, 245...

32 CFR 1701.4 - Privacy Act responsibilities/policy.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Privacy Act responsibilities/policy. 1701.4... NATIONAL INTELLIGENCE ADMINISTRATION OF RECORDS UNDER THE PRIVACY ACT OF 1974 Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 1701.4 Privacy Act responsibilities/policy...

32 CFR 1701.4 - Privacy Act responsibilities/policy.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false Privacy Act responsibilities/policy. 1701.4... NATIONAL INTELLIGENCE ADMINISTRATION OF RECORDS UNDER THE PRIVACY ACT OF 1974 Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 1701.4 Privacy Act responsibilities/policy...

17 CFR 160.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2011 CFR

2011-04-01

... privacy notices. 160.6 Section 160.6 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.6 Information to be included in privacy notices. (a) General rule. The initial, annual, and revised privacy notices that you...

32 CFR 1701.4 - Privacy Act responsibilities/policy.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 6 2012-07-01 2012-07-01 false Privacy Act responsibilities/policy. 1701.4... NATIONAL INTELLIGENCE ADMINISTRATION OF RECORDS UNDER THE PRIVACY ACT OF 1974 Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 1701.4 Privacy Act responsibilities/policy...

16 CFR 313.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 16 Commercial Practices 1 2014-01-01 2014-01-01 false Initial privacy notice to consumers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.4 Initial privacy... notice that accurately reflects your privacy policies and practices to: (1) Customer. An individual who...

16 CFR 313.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Annual privacy notice to customers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.5 Annual privacy... customers that accurately reflects your privacy policies and practices not less than annually during the...

16 CFR 313.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 16 Commercial Practices 1 2012-01-01 2012-01-01 false Annual privacy notice to customers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.5 Annual privacy... customers that accurately reflects your privacy policies and practices not less than annually during the...

6 CFR 1002.4 - Responses to Privacy Act requests.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 6 Domestic Security 1 2014-01-01 2014-01-01 false Responses to Privacy Act requests. 1002.4 Section 1002.4 Domestic Security PRIVACY AND CIVIL LIBERTIES OVERSIGHT BOARD IMPLEMENTATION OF THE PRIVACY ACT OF 1974 § 1002.4 Responses to Privacy Act requests. (a) Acknowledgement. The Privacy Act Officer...

17 CFR 160.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 1 2011-04-01 2011-04-01 false Initial privacy notice to... COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.4 Initial privacy... notice that accurately reflects your privacy policies and practices to: (1) Customer. An individual who...

32 CFR 1701.4 - Privacy Act responsibilities/policy.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 6 2014-07-01 2014-07-01 false Privacy Act responsibilities/policy. 1701.4... NATIONAL INTELLIGENCE ADMINISTRATION OF RECORDS UNDER THE PRIVACY ACT OF 1974 Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 1701.4 Privacy Act responsibilities/policy...

17 CFR 160.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 1 2011-04-01 2011-04-01 false Annual privacy notice to... COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.5 Annual privacy... customers that accurately reflects your privacy policies and practices not less than annually during the...

16 CFR 313.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 16 Commercial Practices 1 2014-01-01 2014-01-01 false Annual privacy notice to customers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.5 Annual privacy... customers that accurately reflects your privacy policies and practices not less than annually during the...

16 CFR 313.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Initial privacy notice to consumers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.4 Initial privacy... notice that accurately reflects your privacy policies and practices to: (1) Customer. An individual who...

16 CFR 313.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 16 Commercial Practices 1 2012-01-01 2012-01-01 false Initial privacy notice to consumers required... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.4 Initial privacy... notice that accurately reflects your privacy policies and practices to: (1) Customer. An individual who...

32 CFR 1701.4 - Privacy Act responsibilities/policy.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Privacy Act responsibilities/policy. 1701.4... NATIONAL INTELLIGENCE ADMINISTRATION OF RECORDS UNDER THE PRIVACY ACT OF 1974 Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 1701.4 Privacy Act responsibilities/policy...

The Privacy Jungle:On the Market for Data Protection in Social Networks

NASA Astrophysics Data System (ADS)

Bonneau, Joseph; Preibusch, Sören

We have conducted the first thorough analysis of the market for privacy practices and policies in online social networks. From an evaluation of 45 social networking sites using 260 criteria we find that many popular assumptions regarding privacy and social networking need to be revisited when considering the entire ecosystem instead of only a handful of well-known sites. Contrary to the common perception of an oligopolistic market, we find evidence of vigorous competition for new users. Despite observing many poor security practices, there is evidence that social network providers are making efforts to implement privacy enhancing technologies with substantial diversity in the amount of privacy control offered. However, privacy is rarely used as a selling point, even then only as auxiliary, nondecisive feature. Sites also failed to promote their existing privacy controls within the site. We similarly found great diversity in the length and content of formal privacy policies, but found an opposite promotional trend: though almost all policies are not accessible to ordinary users due to obfuscating legal jargon, they conspicuously vaunt the sites' privacy practices. We conclude that the market for privacy in social networks is dysfunctional in that there is significant variation in sites' privacy controls, data collection requirements, and legal privacy policies, but this is not effectively conveyed to users. Our empirical findings motivate us to introduce the novel model of a privacy communication game, where the economically rational choice for a site operator is to make privacy control available to evade criticism from privacy fundamentalists, while hiding the privacy control interface and privacy policy to maximize sign-up numbers and encourage data sharing from the pragmatic majority of users.

13 CFR 102.39 - SBA's exempt Privacy Act systems of records.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false SBA's exempt Privacy Act systems... DISCLOSURE AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.39 SBA's exempt Privacy Act systems of records. (a) Systems of records subject to investigatory...

12 CFR 1204.2 - What do the terms in this part mean?

Code of Federal Regulations, 2011 CFR

2011-01-01

... appeals. Privacy Act Officer means the FHFA employee who has primary responsibility for privacy and data... Section 1204.2 Banks and Banking FEDERAL HOUSING FINANCE AGENCY ORGANIZATION AND OPERATIONS PRIVACY ACT... Enterprise Oversight. Privacy Act means the Privacy Act of 1974, as amended (5 U.S.C. 552a). Privacy Act...

16 CFR 313.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 16 Commercial Practices 1 2013-01-01 2013-01-01 false Model privacy form and examples. 313.2... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 313.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

12 CFR 332.8 - Revised privacy notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 5 2014-01-01 2014-01-01 false Revised privacy notices. 332.8 Section 332.8... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.8 Revised privacy notices. (a... described in your prior notice. (c) Delivery. When you are required to deliver a revised privacy notice by...

12 CFR 332.2 - Model privacy form and examples.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 5 2012-01-01 2012-01-01 false Model privacy form and examples. 332.2 Section... POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 332.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions...

12 CFR 216.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 2 2014-01-01 2014-01-01 false Model privacy form and examples. 216.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 216.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the...

32 CFR 311.7 - OSD/JS Privacy Office Processes.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 2 2011-07-01 2011-07-01 false OSD/JS Privacy Office Processes. 311.7 Section...) PRIVACY PROGRAM OFFICE OF THE SECRETARY OF DEFENSE AND JOINT STAFF PRIVACY PROGRAM § 311.7 OSD/JS Privacy Office Processes. The OSD/JS Privacy Office shall: (a) Exercise oversight and administrative control of...

13 CFR 102.36 - Privacy Act standards of conduct.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 13 Business Credit and Assistance 1 2011-01-01 2011-01-01 false Privacy Act standards of conduct... AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.36 Privacy Act standards of conduct. Each Program/Support Office Head or designee shall inform its...

17 CFR 160.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 1 2013-04-01 2013-04-01 false Model privacy form and... PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT § 160.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this...

16 CFR 313.2 - Model privacy form and examples.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 16 Commercial Practices 1 2012-01-01 2012-01-01 false Model privacy form and examples. 313.2... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 313.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

12 CFR 332.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 5 2014-01-01 2014-01-01 false Model privacy form and examples. 332.2 Section... POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 332.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions...

17 CFR 160.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 1 2011-04-01 2011-04-01 false Delivering privacy and opt out... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.9 Delivering privacy and opt out notices. (a) How to provide notices. You must provide any privacy notices and opt out notices...

32 CFR 806b.4 - Privacy Act complaints.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 6 2014-07-01 2014-07-01 false Privacy Act complaints. 806b.4 Section 806b.4 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT PROGRAM Overview of the Privacy Act Program § 806b.4 Privacy Act complaints. (a) Process Privacy Act...

32 CFR 311.7 - OSD/JS Privacy Office Processes.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 2 2013-07-01 2013-07-01 false OSD/JS Privacy Office Processes. 311.7 Section...) PRIVACY PROGRAM OFFICE OF THE SECRETARY OF DEFENSE AND JOINT STAFF PRIVACY PROGRAM § 311.7 OSD/JS Privacy Office Processes. The OSD/JS Privacy Office shall: (a) Exercise oversight and administrative control of...

13 CFR 102.36 - Privacy Act standards of conduct.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 13 Business Credit and Assistance 1 2012-01-01 2012-01-01 false Privacy Act standards of conduct... AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.36 Privacy Act standards of conduct. Each Program/Support Office Head or designee shall inform its...

16 CFR 313.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Information to be included in privacy... OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.6 Information to be included in privacy notices. (a) General rule. The initial, annual, and revised privacy notices...

32 CFR 311.7 - OSD/JS Privacy Office Processes.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 2 2014-07-01 2014-07-01 false OSD/JS Privacy Office Processes. 311.7 Section...) PRIVACY PROGRAM OFFICE OF THE SECRETARY OF DEFENSE AND JOINT STAFF PRIVACY PROGRAM § 311.7 OSD/JS Privacy Office Processes. The OSD/JS Privacy Office shall: (a) Exercise oversight and administrative control of...

32 CFR 806b.4 - Privacy Act complaints.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false Privacy Act complaints. 806b.4 Section 806b.4 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT PROGRAM Overview of the Privacy Act Program § 806b.4 Privacy Act complaints. (a) Process Privacy Act...

12 CFR 216.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 2 2012-01-01 2012-01-01 false Information to be included in privacy notices... SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.6 Information to be included in privacy notices. (a) General rule. The initial, annual, and revised privacy...

12 CFR 216.2 - Model privacy form and examples.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 2 2012-01-01 2012-01-01 false Model privacy form and examples. 216.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 216.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the...

12 CFR 332.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 5 2012-01-01 2012-01-01 false Revised privacy notices. 332.8 Section 332.8... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.8 Revised privacy notices. (a... described in your prior notice. (c) Delivery. When you are required to deliver a revised privacy notice by...

17 CFR 160.2 - Model privacy form and examples.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 1 2012-04-01 2012-04-01 false Model privacy form and... PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT § 160.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this...

16 CFR 313.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 16 Commercial Practices 1 2014-01-01 2014-01-01 false Model privacy form and examples. 313.2... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 313.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

12 CFR 216.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 2 2013-01-01 2013-01-01 false Model privacy form and examples. 216.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 216.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the...

16 CFR 313.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 16 Commercial Practices 1 2012-01-01 2012-01-01 false Information to be included in privacy... OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.6 Information to be included in privacy notices. (a) General rule. The initial, annual, and revised privacy notices...

13 CFR 102.36 - Privacy Act standards of conduct.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 13 Business Credit and Assistance 1 2014-01-01 2014-01-01 false Privacy Act standards of conduct... AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.36 Privacy Act standards of conduct. Each Program/Support Office Head or designee shall inform its...

17 CFR 160.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 1 2011-04-01 2011-04-01 false Model privacy form and... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 160.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

32 CFR 806b.4 - Privacy Act complaints.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false Privacy Act complaints. 806b.4 Section 806b.4 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT PROGRAM Overview of the Privacy Act Program § 806b.4 Privacy Act complaints. (a) Process Privacy Act...

16 CFR 313.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 16 Commercial Practices 1 2014-01-01 2014-01-01 false Information to be included in privacy... OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.6 Information to be included in privacy notices. (a) General rule. The initial, annual, and revised privacy notices...

16 CFR 313.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Model privacy form and examples. 313.2... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 313.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

16 CFR 313.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Delivering privacy and opt out notices. 313... CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.9 Delivering privacy and opt out notices. (a) How to provide notices. You must provide any privacy notices and opt out...

12 CFR 332.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 4 2011-01-01 2011-01-01 false Model privacy form and examples. 332.2 Section... POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 332.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in Appendix A of this part, consistent with the instructions...

12 CFR 332.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Revised privacy notices. 332.8 Section 332.8... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.8 Revised privacy notices. (a... described in your prior notice. (c) Delivery. When you are required to deliver a revised privacy notice by...

13 CFR 102.36 - Privacy Act standards of conduct.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 13 Business Credit and Assistance 1 2013-01-01 2013-01-01 false Privacy Act standards of conduct... AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.36 Privacy Act standards of conduct. Each Program/Support Office Head or designee shall inform its...

17 CFR 160.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 2 2014-04-01 2014-04-01 false Model privacy form and... (CONTINUED) PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT § 160.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of...

12 CFR 332.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Model privacy form and examples. 332.2 Section... POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 332.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions...

32 CFR 311.7 - OSD/JS Privacy Office Processes.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 2 2012-07-01 2012-07-01 false OSD/JS Privacy Office Processes. 311.7 Section...) PRIVACY PROGRAM OFFICE OF THE SECRETARY OF DEFENSE AND JOINT STAFF PRIVACY PROGRAM § 311.7 OSD/JS Privacy Office Processes. The OSD/JS Privacy Office shall: (a) Exercise oversight and administrative control of...

12 CFR 216.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 2 2011-01-01 2011-01-01 false Model privacy form and examples. 216.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 216.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the...

32 CFR 806b.4 - Privacy Act complaints.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 6 2012-07-01 2012-07-01 false Privacy Act complaints. 806b.4 Section 806b.4 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT PROGRAM Overview of the Privacy Act Program § 806b.4 Privacy Act complaints. (a) Process Privacy Act...

12 CFR 332.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 4 2011-01-01 2011-01-01 false Revised privacy notices. 332.8 Section 332.8... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.8 Revised privacy notices. (a... described in your prior notice. (c) Delivery. When you are required to deliver a revised privacy notice by...

12 CFR 332.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Information to be included in privacy notices... OF GENERAL POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.6 Information to be included in privacy notices. (a) General rule. The initial, annual and revised privacy...

13 CFR 102.36 - Privacy Act standards of conduct.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 13 Business Credit and Assistance 1 2010-01-01 2010-01-01 false Privacy Act standards of conduct... AND PRIVACY Protection of Privacy and Access to Individual Records Under the Privacy Act of 1974 § 102.36 Privacy Act standards of conduct. Each Program/Support Office Head or designee shall inform its...

12 CFR 216.2 - Model privacy form and examples.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 2 2010-01-01 2010-01-01 false Model privacy form and examples. 216.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 216.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the...

17 CFR 160.2 - Model privacy form and examples.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Model privacy form and... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 160.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

16 CFR 313.2 - Model privacy form and examples.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Model privacy form and examples. 313.2... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 313.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendix A of this part, consistent with the instructions in appendix...

76 FR 67763 - Privacy Act of 1974; Privacy Act System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-02

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice (11-109)] Privacy Act of 1974; Privacy Act... proposed revisions to an existing Privacy Act system of records. SUMMARY: Pursuant to the provisions of the Privacy Act of 1974 (5 U.S.C. 552a), the National Aeronautics and Space Administration is issuing public...

12 CFR 332.8 - Revised privacy notices.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Revised privacy notices. 332.8 Section 332.8... PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.8 Revised privacy notices. (a... described in your prior notice. (c) Delivery. When you are required to deliver a revised privacy notice by...

32 CFR 806b.4 - Privacy Act complaints.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Privacy Act complaints. 806b.4 Section 806b.4 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT PROGRAM Overview of the Privacy Act Program § 806b.4 Privacy Act complaints. (a) Process Privacy Act...

12 CFR 332.2 - Model privacy form and examples.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 4 2010-01-01 2010-01-01 false Model privacy form and examples. 332.2 Section... POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 332.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in Appendix A of this part, consistent with the instructions...

12 CFR 716.5 - Annual privacy notice to members required.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Annual privacy notice to members required. 716... UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.5 Annual privacy... members that accurately reflects your privacy policies and practices not less than annually during the...

75 FR 7979 - Privacy Act of 1974: Implementation of Exemptions; Department of Homeland Security/ALL-027 The...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-23

... privacy issues please contact: Mary Ellen Callahan (703-235-0780), Chief Privacy Officer, Privacy Office...] Privacy Act of 1974: Implementation of Exemptions; Department of Homeland Security/ALL-027 The History of the Department of Homeland Security System of Records AGENCY: Privacy Office, DHS. ACTION: Notice of...

32 CFR 311.7 - OSD/JS Privacy Office Processes.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false OSD/JS Privacy Office Processes. 311.7 Section...) PRIVACY PROGRAM OFFICE OF THE SECRETARY OF DEFENSE AND JOINT STAFF PRIVACY PROGRAM § 311.7 OSD/JS Privacy Office Processes. The OSD/JS Privacy Office shall: (a) Exercise oversight and administrative control of...

The role of privacy protection in healthcare information systems adoption.

PubMed

Hsu, Chien-Lung; Lee, Ming-Ren; Su, Chien-Hui

2013-10-01

Privacy protection is an important issue and challenge in healthcare information systems (HISs). Recently, some privacy-enhanced HISs are proposed. Users' privacy perception, intention, and attitude might affect the adoption of such systems. This paper aims to propose a privacy-enhanced HIS framework and investigate the role of privacy protection in HISs adoption. In the proposed framework, privacy protection, access control, and secure transmission modules are designed to enhance the privacy protection of a HIS. An experimental privacy-enhanced HIS is also implemented. Furthermore, we proposed a research model extending the unified theory of acceptance and use of technology by considering perceived security and information security literacy and then investigate user adoption of a privacy-enhanced HIS. The experimental results and analyses showed that user adoption of a privacy-enhanced HIS is directly affected by social influence, performance expectancy, facilitating conditions, and perceived security. Perceived security has a mediating effect between information security literacy and user adoption. This study proposes several implications for research and practice to improve designing, development, and promotion of a good healthcare information system with privacy protection.

Biobank research and the right to privacy.

PubMed

Ursin, Lars Oystein

2008-01-01

What is privacy? What does privacy mean in relation to biobanking, in what way do the participants have an interest in privacy, (why) is there a right to privacy, and how should the privacy issue be regulated when it comes to biobank research? A relational view of privacy is argued for in this article, which takes as its basis a general discussion of several concepts of privacy and attempts at grounding privacy rights. In promoting and protecting the rights that participants in biobank research might have to privacy, it is argued that their interests should be related to the specific context of the provision and reception of health care that participation in biobank research is connected with. Rather than just granting participants an exclusive right to or ownership of their health information, which must be waived in order to make biobank research possible, the privacy aspect of health information should be viewed in light of the moral rights and duties that accompany any involvement in a research based system of health services.

12 CFR 716.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 6 2011-01-01 2011-01-01 false Model privacy form and examples. 716.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 716.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in appendixA of this part, consistent with the instructions in appendixA...

12 CFR 332.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Delivering privacy and opt out notices. 332.9... GENERAL POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.9 Delivering privacy and opt out notices. (a) How to provide notices. You must provide any privacy notices and opt out...

12 CFR 716.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 7 2013-01-01 2013-01-01 false Model privacy form and examples. 716.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 716.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in Appendix A of this part, consistent with the instructions in Appendix...

12 CFR 716.2 - Model privacy form and examples.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 7 2012-01-01 2012-01-01 false Model privacy form and examples. 716.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 716.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in Appendix A of this part, consistent with the instructions in Appendix...

41 CFR 105-64.801 - How to file a privacy complaint.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 41 Public Contracts and Property Management 3 2011-01-01 2011-01-01 false How to file a privacy... Administration 64-GSA PRIVACY ACT RULES 64.8-Privacy Complaints § 105-64.801 How to file a privacy complaint. E-mail your complaint to [email protected] or send to: GSA Privacy Act Officer (CIB), General...

41 CFR 105-64.801 - How to file a privacy complaint.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 41 Public Contracts and Property Management 3 2012-01-01 2012-01-01 false How to file a privacy... Administration 64-GSA PRIVACY ACT RULES 64.8-Privacy Complaints § 105-64.801 How to file a privacy complaint. E-mail your complaint to [email protected] or send to: GSA Privacy Act Officer (CIB), General...

12 CFR 216.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 2 2012-01-01 2012-01-01 false Delivering privacy and opt out notices. 216.9... PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.9 Delivering privacy and opt out notices. (a) How to provide notices. You must provide any privacy notices and opt out...

41 CFR 105-64.801 - How to file a privacy complaint.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 41 Public Contracts and Property Management 3 2010-07-01 2010-07-01 false How to file a privacy... Administration 64-GSA PRIVACY ACT RULES 64.8-Privacy Complaints § 105-64.801 How to file a privacy complaint. E-mail your complaint to [email protected] or send to: GSA Privacy Act Officer (CIB), General...

12 CFR 716.2 - Model privacy form and examples.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Model privacy form and examples. 716.2 Section... PRIVACY OF CONSUMER FINANCIAL INFORMATION § 716.2 Model privacy form and examples. (a) Model privacy form. Use of the model privacy form in Appendix A of this part, consistent with the instructions in Appendix...

Couldn't or wouldn't? The influence of privacy concerns and self-efficacy in privacy management on privacy protection.

PubMed

Chen, Hsuan-Ting; Chen, Wenghong

2015-01-01

Sampling 515 college students, this study investigates how privacy protection, including profile visibility, self-disclosure, and friending, are influenced by privacy concerns and efficacy regarding one's own ability to manage privacy settings, a factor that researchers have yet to give a great deal of attention to in the context of social networking sites (SNSs). The results of this study indicate an inconsistency in adopting strategies to protect privacy, a disconnect from limiting profile visibility and friending to self-disclosure. More specifically, privacy concerns lead SNS users to limit their profile visibility and discourage them from expanding their network. However, they do not constrain self-disclosure. Similarly, while self-efficacy in privacy management encourages SNS users to limit their profile visibility, it facilitates self-disclosure. This suggests that if users are limiting their profile visibility and constraining their friending behaviors, it does not necessarily mean they will reduce self-disclosure on SNSs because these behaviors are predicted by different factors. In addition, the study finds an interaction effect between privacy concerns and self-efficacy in privacy management on friending. It points to the potential problem of increased risk-taking behaviors resulting from high self-efficacy in privacy management and low privacy concerns.

78 FR 40515 - Privacy Act of 1974; Privacy Act System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

... NATIONAL AERONAUTICS AND SPACE ADMINISTRATION [Notice 13-071] Privacy Act of 1974; Privacy Act System of Records AGENCY: National Aeronautics and Space Administration (NASA). ACTION: Notice of Privacy... training associated with [[Page 40516

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

PubMed Central

Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-01-01

Background Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. PMID:28935617

32 CFR 806b.11 - When to give Privacy Act Statements (PAS).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 6 2011-07-01 2011-07-01 false When to give Privacy Act Statements (PAS). 806b... ADMINISTRATION PRIVACY ACT PROGRAM Collecting Personal Information § 806b.11 When to give Privacy Act Statements... information. Give a copy of the Privacy Act Statement if asked. Do not ask the person to sign the Privacy Act...

32 CFR 806b.11 - When to give Privacy Act Statements (PAS).

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false When to give Privacy Act Statements (PAS). 806b... ADMINISTRATION PRIVACY ACT PROGRAM Collecting Personal Information § 806b.11 When to give Privacy Act Statements... information. Give a copy of the Privacy Act Statement if asked. Do not ask the person to sign the Privacy Act...

32 CFR 806b.11 - When to give Privacy Act Statements (PAS).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false When to give Privacy Act Statements (PAS). 806b... ADMINISTRATION PRIVACY ACT PROGRAM Collecting Personal Information § 806b.11 When to give Privacy Act Statements... information. Give a copy of the Privacy Act Statement if asked. Do not ask the person to sign the Privacy Act...

Big issues, small systems: managing with information in medical research.

PubMed

Jones, J; Preston, H

2000-08-01

This subject of this article is the design of a database system for handling files related to the work of the Molecular Genetics Department of the International Blood Group Reference Laboratory. It examines specialist information needs identified within this organization and it indicates how the design of the Rhesus Information Tracking System was able to meet current needs. Rapid Applications Development prototyping forms the basis of the investigation, linked to interview, questionnaire, and observation techniques in order to establish requirements for interoperability. In particular, the place of this specialist database within the much broader information strategy of the National Blood Service will be examined. This unique situation is analogous to management activities in broader environments and a number of generic issues are highlighted by the research.

Design of optimal and ideal 2-D concentrators with the collector immersed in a dielectric tube

NASA Astrophysics Data System (ADS)

Minano, J. C.; Ruiz, J. M.; Luque, A.

1983-12-01

A method is presented for designing ideal and optimal 2-D concentrators when the collector is placed inside a dielectric tube, for the particular case of a bifacial solar collector. The prototype 2-D (cylindrical geometry) concentrator is the compound parabolic concentrator or CPC, and from the beginning of development, it was found by Winston (1978) that filling up the concentrator with a transparent dielectric medium results in a big improvement of the optical properties. The method reported here is based on the extreme ray principle of design and avoids the use of differential equations by means of a proper appliction of Fermat's principle. One advantage of these concentrators is that they allow the size to be small compared with classical CPCs.

Privacy information management for video surveillance

NASA Astrophysics Data System (ADS)

Luo, Ying; Cheung, Sen-ching S.

2013-05-01

The widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been proposed to automatically redact images of trusted individuals in the surveillance video. To identify these individuals for protection, the most reliable approach is to use biometric signals such as iris patterns as they are immutable and highly discriminative. In this paper, we propose a privacy data management system to be used in a privacy-aware video surveillance system. The privacy status of a subject is anonymously determined based on her iris pattern. For a trusted subject, the surveillance video is redacted and the original imagery is considered to be the privacy information. Our proposed system allows a subject to access her privacy information via the same biometric signal for privacy status determination. Two secure protocols, one for privacy information encryption and the other for privacy information retrieval are proposed. Error control coding is used to cope with the variability in iris patterns and efficient implementation is achieved using surrogate data records. Experimental results on a public iris biometric database demonstrate the validity of our framework.

Position-sensitive ``movie'' in situ neutron detector for the UCN τ experiment

NASA Astrophysics Data System (ADS)

Weaver, Hannah; UCNTau Collaboration

2016-09-01

Precision measurements of neutron β-decay parameters provide tests of fundamental theories in elementary particle physics and cosmology such as the Standard Model and Big Bang nucleosynthesis. In particular, the UCN τ experiment aims to measure the mean lifetime of ultracold neutrons confined in an asymmetric magneto-gravitational trap using an in situ neutron detector. This detector consists of a 20 nm film of 10B on top of a ZnS:Ag scintillating screen. The screen is readout using two photomultipliers which view an array of wavelength shifting fibers optically coupled to the scintillator. When the detector is lowered into the loaded trap, light is emitted due to the charged particles recoiling into the ZnS:Ag when neutrons absorb on the 10B. Phase space evolution in the stored neutron population can lead to apparent shifts in the measured neutron lifetime with the detector height. In order to quantify this systematic uncertainty, we are implementing a supplemental 64-channel position-sensitive PMT module with high quantum efficiency and fast time response to image the entire detector in situ during measurements. We have characterized a prototype using a ZnS screen and an α-particle source along with a prototype lens system and will report the results and future plans.

Somali asylum seekers' perceptions of privacy in healthcare.

PubMed

Eklöf, Niina; Abdulkarim, Hibag; Hupli, Maija; Leino-Kilpi, Helena

2016-08-01

Privacy has been recognized as a basic human right and a part of quality of care. However, little is known about the privacy of Somali asylum seekers in healthcare, even though they are one of the largest asylum seeker groups in the world. The aim of the study was to describe the content and importance of privacy and its importance in healthcare from the perspective of Somali asylum seekers. The data of this explorative qualitative study were collected by four focus group interviews with 18 Somali asylum seekers with the help of an interpreter. The data were analysed by inductive content analysis. Research permissions were obtained from the director of the reception centre and from the Department of Social Services. Ethical approval was obtained from the Ethics Committee of Turku University. The content of privacy includes visual privacy, physical privacy and informational privacy. All contents can be shared with healthcare professionals. The importance of privacy includes respect, dignity and freedom. Privacy is strongly connected to the collectivism of Somali culture and religion. Unlike the Western cultures, privacy is not important only for the individual; most of all, it is seen to support collectivism. Even though all contents of privacy can be shared with healthcare professionals, it is important to recognize the cultural aspect of privacy especially when using interpreters with Somali background. © The Author(s) 2015.

Context-Aware Generative Adversarial Privacy

NASA Astrophysics Data System (ADS)

Huang, Chong; Kairouz, Peter; Chen, Xiao; Sankar, Lalitha; Rajagopal, Ram

2017-12-01

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

75 FR 28051 - Public Workshop: Pieces of Privacy

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-19

... DEPARTMENT OF HOMELAND SECURITY Office of the Secretary Public Workshop: Pieces of Privacy AGENCY: Privacy Office, DHS. ACTION: Notice announcing public workshop. SUMMARY: The Department of Homeland Security Privacy Office will host a public workshop, ``Pieces of Privacy.'' DATES: The workshop will be...

A new standardized data collection system for interdisciplinary thyroid cancer management: Thyroid COBRA.

PubMed

Tagliaferri, Luca; Gobitti, Carlo; Colloca, Giuseppe Ferdinando; Boldrini, Luca; Farina, Eleonora; Furlan, Carlo; Paiar, Fabiola; Vianello, Federica; Basso, Michela; Cerizza, Lorenzo; Monari, Fabio; Simontacchi, Gabriele; Gambacorta, Maria Antonietta; Lenkowicz, Jacopo; Dinapoli, Nicola; Lanzotti, Vito; Mazzarotto, Renzo; Russi, Elvio; Mangoni, Monica

2018-07-01

The big data approach offers a powerful alternative to Evidence-based medicine. This approach could guide cancer management thanks to machine learning application to large-scale data. Aim of the Thyroid CoBRA (Consortium for Brachytherapy Data Analysis) project is to develop a standardized web data collection system, focused on thyroid cancer. The Metabolic Radiotherapy Working Group of Italian Association of Radiation Oncology (AIRO) endorsed the implementation of a consortium directed to thyroid cancer management and data collection. The agreement conditions, the ontology of the collected data and the related software services were defined by a multicentre ad hoc working-group (WG). Six Italian cancer centres were firstly started the project, defined and signed the Thyroid COBRA consortium agreement. Three data set tiers were identified: Registry, Procedures and Research. The COBRA-Storage System (C-SS) appeared to be not time-consuming and to be privacy respecting, as data can be extracted directly from the single centre's storage platforms through a secured connection that ensures reliable encryption of sensible data. Automatic data archiving could be directly performed from Image Hospital Storage System or the Radiotherapy Treatment Planning Systems. The C-SS architecture will allow "Cloud storage way" or "distributed learning" approaches for predictive model definition and further clinical decision support tools development. The development of the Thyroid COBRA data Storage System C-SS through a multicentre consortium approach appeared to be a feasible tool in the setup of complex and privacy saving data sharing system oriented to the management of thyroid cancer and in the near future every cancer type. Copyright © 2018 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

PubMed

Deist, Timo M; Jochems, A; van Soest, Johan; Nalbantov, Georgi; Oberije, Cary; Walsh, Seán; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Dekker, Andre; Lambin, Philippe

2017-06-01

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

47 CFR 64.1601 - Delivery requirements and privacy restrictions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 3 2011-10-01 2011-10-01 false Delivery requirements and privacy restrictions... Number; Privacy § 64.1601 Delivery requirements and privacy restrictions. (a) Delivery. Except as... party number (CPN) associated with an interstate call to interconnecting carriers. (b) Privacy. Except...

75 FR 20298 - Privacy Act Regulations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-19

... Part 200 RIN 0430-AA03 Privacy Act Regulations AGENCY: Recovery Accountability and Transparency Board... amend the Board's regulations implementing the Privacy Act of 1974 (Privacy Act), as amended. This proposed rule would exempt certain systems of records from certain sections of the Privacy Act. These...

47 CFR 64.1601 - Delivery requirements and privacy restrictions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 3 2010-10-01 2010-10-01 false Delivery requirements and privacy restrictions... Number; Privacy § 64.1601 Delivery requirements and privacy restrictions. (a) Delivery. Except as... party number (CPN) associated with an interstate call to interconnecting carriers. (b) Privacy. Except...

Privacy Issues of a National Research and Education Network.

ERIC Educational Resources Information Center

Katz, James E.; Graveman, Richard F.

1991-01-01

Discussion of the right to privacy of communications focuses on privacy expectations within a National Research and Education Network (NREN). Highlights include privacy needs in scientific and education communications; academic and research networks; network security and privacy concerns; protection strategies; and consequences of privacy…

Digital Privacy: Toward a New Politics and Discursive Practice.

ERIC Educational Resources Information Center

Doty, Philip

2001-01-01

Discussion of privacy focuses on digital environments and a more inclusive understanding of privacy. Highlights include legal and policy conceptions; legislation protecting privacy; relevant Supreme Court cases; torts and privacy; European and other efforts; surveillance and social control; information entrepreneurialism; Jurgen Habermas; free…

More Than Defense in Daily Experience of Privacy: The Functions of Privacy in Digital and Physical Environments

PubMed Central

Lombardi, Debora Benedetta; Ciceri, Maria Rita

2016-01-01

The purpose of the current study was to investigate the experience of privacy, focusing on its functional role in personal well-being. A sample (N = 180) comprised subjects between 18 and 50 years of age were asked to spontaneously provide accounts of their experiences with privacy and answer close-ended questions to acquire a description of a daily experience of privacy. The results showed the importance attributed to the function of privacy related to the “defense from social threats”, and the twofold function of privacy related to an “achieved state of privacy”, in the terms of both “system maintenance” and “system development”. The results also shed light on the role of the environment in shaping one’s experience of privacy. Specifically, the participants recognized more easily the function of defense from threats related to seeking privacy while interacting in digital environments, whereas they seemed to benefit from positive functions related to an achieved state of privacy in physical environments. The findings sustain the notion of privacy as a supportive condition for some psychological processes involved in the positive human functioning and confirm previous studies conducted on the role of privacy in human well-being. PMID:27247696

32 CFR 318.5 - Designations and responsibilities

Code of Federal Regulations, 2011 CFR

2011-07-01

... requirements developed to collect and/or maintain personal data conform to DoD Privacy Act Program standards...) PRIVACY PROGRAM DEFENSE THREAT REDUCTION AGENCY PRIVACY PROGRAM § 318.5 Designations and responsibilities... effective Privacy Program. (2) Appoint a senior official to serve as the Agency Privacy Act Officer. (3...

32 CFR 318.5 - Designations and responsibilities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... requirements developed to collect and/or maintain personal data conform to DoD Privacy Act Program standards...) PRIVACY PROGRAM DEFENSE THREAT REDUCTION AGENCY PRIVACY PROGRAM § 318.5 Designations and responsibilities... effective Privacy Program. (2) Appoint a senior official to serve as the Agency Privacy Act Officer. (3...

48 CFR 39.105 - Privacy.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 48 Federal Acquisition Regulations System 1 2012-10-01 2012-10-01 false Privacy. 39.105 Section 39... CONTRACTING ACQUISITION OF INFORMATION TECHNOLOGY General 39.105 Privacy. Agencies shall ensure that contracts for information technology address protection of privacy in accordance with the Privacy Act (5 U.S.C...

47 CFR 64.1601 - Delivery requirements and privacy restrictions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 3 2014-10-01 2014-10-01 false Delivery requirements and privacy restrictions... Number; Privacy § 64.1601 Delivery requirements and privacy restrictions. (a) Delivery. Except as... and transmission technology used by the carrier or VoIP provider. (b) Privacy. Except as provided in...

32 CFR 310.43 - Privacy Act inspections.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 2 2013-07-01 2013-07-01 false Privacy Act inspections. 310.43 Section 310.43 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Inspections § 310.43 Privacy Act inspections. During internal inspections...

32 CFR 310.43 - Privacy Act inspections.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 2 2014-07-01 2014-07-01 false Privacy Act inspections. 310.43 Section 310.43 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Inspections § 310.43 Privacy Act inspections. During internal inspections...

47 CFR 64.1601 - Delivery requirements and privacy restrictions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 3 2012-10-01 2012-10-01 false Delivery requirements and privacy restrictions... Number; Privacy § 64.1601 Delivery requirements and privacy restrictions. (a) Delivery. Except as... and transmission technology used by the carrier or VoIP provider. (b) Privacy. Except as provided in...

48 CFR 39.105 - Privacy.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 48 Federal Acquisition Regulations System 1 2014-10-01 2014-10-01 false Privacy. 39.105 Section 39... CONTRACTING ACQUISITION OF INFORMATION TECHNOLOGY General 39.105 Privacy. Agencies shall ensure that contracts for information technology address protection of privacy in accordance with the Privacy Act (5 U.S.C...

32 CFR 310.43 - Privacy Act inspections.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 2 2011-07-01 2011-07-01 false Privacy Act inspections. 310.43 Section 310.43 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Inspections § 310.43 Privacy Act inspections. During internal inspections...

47 CFR 64.1601 - Delivery requirements and privacy restrictions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 3 2013-10-01 2013-10-01 false Delivery requirements and privacy restrictions... Number; Privacy § 64.1601 Delivery requirements and privacy restrictions. (a) Delivery. Except as... and transmission technology used by the carrier or VoIP provider. (b) Privacy. Except as provided in...

48 CFR 39.105 - Privacy.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 48 Federal Acquisition Regulations System 1 2011-10-01 2011-10-01 false Privacy. 39.105 Section 39... CONTRACTING ACQUISITION OF INFORMATION TECHNOLOGY General 39.105 Privacy. Agencies shall ensure that contracts for information technology address protection of privacy in accordance with the Privacy Act (5 U.S.C...

48 CFR 39.105 - Privacy.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 48 Federal Acquisition Regulations System 1 2013-10-01 2013-10-01 false Privacy. 39.105 Section 39... CONTRACTING ACQUISITION OF INFORMATION TECHNOLOGY General 39.105 Privacy. Agencies shall ensure that contracts for information technology address protection of privacy in accordance with the Privacy Act (5 U.S.C...

32 CFR 310.43 - Privacy Act inspections.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 2 2012-07-01 2012-07-01 false Privacy Act inspections. 310.43 Section 310.43 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Inspections § 310.43 Privacy Act inspections. During internal inspections...

32 CFR 310.43 - Privacy Act inspections.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 2 2010-07-01 2010-07-01 false Privacy Act inspections. 310.43 Section 310.43 National Defense Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY PROGRAM Inspections § 310.43 Privacy Act inspections. During internal inspections...

Where Big Data and Prediction Meet

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahrens, James; Brase, Jim M.; Hart, Bill

Our ability to assemble and analyze massive data sets, often referred to under the title of “big data”, is an increasingly important tool for shaping national policy. This in turn has introduced issues from privacy concerns to cyber security. But as IBM’s John Kelly emphasized in the last Innovation, making sense of the vast arrays of data will require radically new computing tools. In the past, technologies and tools for analysis of big data were viewed as quite different from the traditional realm of high performance computing (HPC) with its huge models of phenomena such as global climate or supportingmore » the nuclear test moratorium. Looking ahead, this will change with very positive benefits for both worlds. Societal issues such as global security, economic planning and genetic analysis demand increased understanding that goes beyond existing data analysis and reduction. The modeling world often produces simulations that are complex compositions of mathematical models and experimental data. This has resulted in outstanding successes such as the annual assessment of the state of the US nuclear weapons stockpile without underground nuclear testing. Ironically, while there were historically many test conducted, this body of data provides only modest insight into the underlying physics of the system. A great deal of emphasis was thus placed on the level of confidence we can develop for the predictions. As data analytics and simulation come together, there is a growing need to assess the confidence levels in both data being gathered and the complex models used to make predictions. An example of this is assuring the security or optimizing the performance of critical infrastructure systems such as the power grid. If one wants to understand the vulnerabilities of the system or impacts of predicted threats, full scales tests of the grid against threat scenarios are unlikely. Preventive measures would need to be predicated on well-defined margins of confidence in order to take mitigating actions that could have wide ranging impacts. There is a rich opportunity for interaction and exchange between the HPC simulation and data analytics communities.« less

Deriving Requirements for Pervasive Well-Being Technology From Work Stress and Intervention Theory: Framework and Case Study

PubMed Central

Koldijk, Saskia; Kraaij, Wessel

2016-01-01

Background Stress in office environments is a big concern, often leading to burn-out. New technologies are emerging, such as easily available sensors, contextual reasoning, and electronic coaching (e-coaching) apps. In the Smart Reasoning for Well-being at Home and at Work (SWELL) project, we explore the potential of using such new pervasive technologies to provide support for the self-management of well-being, with a focus on individuals' stress-coping. Ideally, these new pervasive systems should be grounded in existing work stress and intervention theory. However, there is a large diversity of theories and they hardly provide explicit directions for technology design. Objective The aim of this paper is to present a comprehensive and concise framework that can be used to design pervasive technologies that support knowledge workers to decrease stress. Methods Based on a literature study we identify concepts relevant to well-being at work and select different work stress models to find causes of work stress that can be addressed. From a technical perspective, we then describe how sensors can be used to infer stress and the context in which it appears, and use intervention theory to further specify interventions that can be provided by means of pervasive technology. Results The resulting general framework relates several relevant theories: we relate “engagement and burn-out” to “stress”, and describe how relevant aspects can be quantified by means of sensors. We also outline underlying causes of work stress and how these can be addressed with interventions, in particular utilizing new technologies integrating behavioral change theory. Based upon this framework we were able to derive requirements for our case study, the pervasive SWELL system, and we implemented two prototypes. Small-scale user studies proved the value of the derived technology-supported interventions. Conclusions The presented framework can be used to systematically develop theory-based technology-supported interventions to address work stress. In the area of pervasive systems for well-being, we identified the following six key research challenges and opportunities: (1) performing multi-disciplinary research, (2) interpreting personal sensor data, (3) relating measurable aspects to burn-out, (4) combining strengths of human and technology, (5) privacy, and (6) ethics. PMID:27380749

A Model Privacy Statement for Ohio Library Web Sites.

ERIC Educational Resources Information Center

Monaco, Michael J.

The purpose of this research was to develop a model privacy policy statement for library World Wide Web sites. First, standards of privacy protection were identified. These standards were culled from the privacy and confidentiality policies of the American Library Association, the Federal Trade Commission's online privacy reports, the guidelines…

45 CFR 164.534 - Compliance dates for initial implementation of the privacy standards.

Code of Federal Regulations, 2010 CFR

2010-10-01

... privacy standards. 164.534 Section 164.534 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES ADMINISTRATIVE DATA STANDARDS AND RELATED REQUIREMENTS SECURITY AND PRIVACY Privacy of Individually Identifiable Health Information § 164.534 Compliance dates for initial implementation of the privacy standards. (a...

45 CFR 164.534 - Compliance dates for initial implementation of the privacy standards.

Code of Federal Regulations, 2014 CFR

2014-10-01

... privacy standards. 164.534 Section 164.534 Public Welfare Department of Health and Human Services ADMINISTRATIVE DATA STANDARDS AND RELATED REQUIREMENTS SECURITY AND PRIVACY Privacy of Individually Identifiable Health Information § 164.534 Compliance dates for initial implementation of the privacy standards. (a...

45 CFR 164.534 - Compliance dates for initial implementation of the privacy standards.

Code of Federal Regulations, 2011 CFR

2011-10-01

... privacy standards. 164.534 Section 164.534 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES ADMINISTRATIVE DATA STANDARDS AND RELATED REQUIREMENTS SECURITY AND PRIVACY Privacy of Individually Identifiable Health Information § 164.534 Compliance dates for initial implementation of the privacy standards. (a...

45 CFR 164.520 - Notice of privacy practices for protected health information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... DATA STANDARDS AND RELATED REQUIREMENTS SECURITY AND PRIVACY Privacy of Individually Identifiable Health Information § 164.520 Notice of privacy practices for protected health information. (a) Standard... 45 Public Welfare 1 2014-10-01 2014-10-01 false Notice of privacy practices for protected health...

32 CFR 310.2 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Privacy Program under 5 U.S.C. 552a and OMB Circular A-130. (b) Authorizes the Defense Privacy Board, the Defense Privacy Board Legal Committee, and the Defense Data Integrity Board. (c) Continues to authorize... Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY...

32 CFR 310.2 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Privacy Program under 5 U.S.C. 552a and OMB Circular A-130. (b) Authorizes the Defense Privacy Board, the Defense Privacy Board Legal Committee, and the Defense Data Integrity Board. (c) Continues to authorize... Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY...

32 CFR 310.2 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Privacy Program under 5 U.S.C. 552a and OMB Circular A-130. (b) Authorizes the Defense Privacy Board, the Defense Privacy Board Legal Committee, and the Defense Data Integrity Board. (c) Continues to authorize... Department of Defense (Continued) OFFICE OF THE SECRETARY OF DEFENSE (CONTINUED) PRIVACY PROGRAM DOD PRIVACY...

76 FR 63896 - Federal Acquisition Regulation; Privacy Training, 2010-013

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-14

... Acquisition Regulation; Privacy Training, 2010-013 AGENCY: Department of Defense (DoD), General Services... contractors to complete training that addresses the protection of privacy, in accordance with the Privacy Act... Regulation (FAR) to add a new subpart 24.3, entitled ``Privacy Training,'' and related clause to ensure that...

32 CFR 505.12 - Privacy Act enforcement actions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 3 2013-07-01 2013-07-01 false Privacy Act enforcement actions. 505.12 Section... AUTHORITIES AND PUBLIC RELATIONS ARMY PRIVACY ACT PROGRAM § 505.12 Privacy Act enforcement actions. (a.... (1) Civil remedies. The DA is subject to civil remedies for violations of the Privacy Act. In...

12 CFR 1016.8 - Revised privacy notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 8 2014-01-01 2014-01-01 false Revised privacy notices. 1016.8 Section 1016.8 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 1016.8 Revised privacy notices. (a) General rule. Except as...

17 CFR 160.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 17 Commodity and Securities Exchanges 1 2011-04-01 2011-04-01 false Revised privacy notices. 160.8 Section 160.8 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.8 Revised privacy notices. (a) General rule. Except...

12 CFR 716.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 7 2012-01-01 2012-01-01 false Revised privacy notices. 716.8 Section 716.8 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING CREDIT UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.8 Revised privacy notices. (a) General...

4 CFR 200.13 - Privacy Act training.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 4 Accounts 1 2014-01-01 2013-01-01 true Privacy Act training. 200.13 Section 200.13 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.13 Privacy Act training. (a) The... any Board systems of records are informed of all requirements necessary to protect the privacy of...

45 CFR 503.1 - Definitions-Privacy Act.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 3 2013-10-01 2013-10-01 false Definitions-Privacy Act. 503.1 Section 503.1... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.1 Definitions—Privacy Act. For the purpose of this part: Agency...

45 CFR 503.1 - Definitions-Privacy Act.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 45 Public Welfare 3 2014-10-01 2014-10-01 false Definitions-Privacy Act. 503.1 Section 503.1... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.1 Definitions—Privacy Act. For the purpose of this part: Agency...

4 CFR 200.4 - Privacy Act inquiries.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 4 Accounts 1 2013-01-01 2013-01-01 false Privacy Act inquiries. 200.4 Section 200.4 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.4 Privacy Act inquiries. (a... Avenue, NW., Suite 700, Washington, DC 20006. Inquiries should be marked “Privacy Act Inquiry” on each...

45 CFR 503.1 - Definitions-Privacy Act.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 45 Public Welfare 3 2012-10-01 2012-10-01 false Definitions-Privacy Act. 503.1 Section 503.1... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.1 Definitions—Privacy Act. For the purpose of this part: Agency...

32 CFR 505.12 - Privacy Act enforcement actions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 32 National Defense 3 2014-07-01 2014-07-01 false Privacy Act enforcement actions. 505.12 Section... AUTHORITIES AND PUBLIC RELATIONS ARMY PRIVACY ACT PROGRAM § 505.12 Privacy Act enforcement actions. (a.... (1) Civil remedies. The DA is subject to civil remedies for violations of the Privacy Act. In...

48 CFR 52.224-2 - Privacy Act.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 48 Federal Acquisition Regulations System 2 2014-10-01 2014-10-01 false Privacy Act. 52.224-2... AND FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Text of Provisions and Clauses 52.224-2 Privacy... agency function: Privacy Act (APR 1984) (a) The Contractor agrees to— (1) Comply with the Privacy Act of...

48 CFR 1452.224-1 - Privacy Act Notification.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 48 Federal Acquisition Regulations System 5 2014-10-01 2014-10-01 false Privacy Act Notification... Privacy Act Notification. (a) As prescribed in 1424.104, the clause at FAR 52.224-1, Privacy Act... the clause to read “Privacy Act Notification (JUL 1996) (Deviation)”; and (2) Adding the following...

32 CFR 505.12 - Privacy Act enforcement actions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 3 2012-07-01 2009-07-01 true Privacy Act enforcement actions. 505.12 Section... AUTHORITIES AND PUBLIC RELATIONS ARMY PRIVACY ACT PROGRAM § 505.12 Privacy Act enforcement actions. (a.... (1) Civil remedies. The DA is subject to civil remedies for violations of the Privacy Act. In...

12 CFR 216.8 - Revised privacy notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 2 2014-01-01 2014-01-01 false Revised privacy notices. 216.8 Section 216.8 Banks and Banking FEDERAL RESERVE SYSTEM BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.8 Revised privacy notices...

12 CFR 1016.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 8 2012-01-01 2012-01-01 false Revised privacy notices. 1016.8 Section 1016.8 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 1016.8 Revised privacy notices. (a) General rule. Except as...

4 CFR 200.4 - Privacy Act inquiries.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 4 Accounts 1 2014-01-01 2013-01-01 true Privacy Act inquiries. 200.4 Section 200.4 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.4 Privacy Act inquiries. (a... Avenue, NW., Suite 700, Washington, DC 20006. Inquiries should be marked “Privacy Act Inquiry” on each...

4 CFR 200.13 - Privacy Act training.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 4 Accounts 1 2012-01-01 2012-01-01 false Privacy Act training. 200.13 Section 200.13 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.13 Privacy Act training. (a) The... any Board systems of records are informed of all requirements necessary to protect the privacy of...

10 CFR 1304.113 - Privacy Act training.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Privacy Act training. 1304.113 Section 1304.113 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.113 Privacy Act training. (a) The Board... Board systems are informed of all requirements necessary to protect the privacy of individuals. The...

10 CFR 1304.113 - Privacy Act training.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 4 2014-01-01 2014-01-01 false Privacy Act training. 1304.113 Section 1304.113 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.113 Privacy Act training. (a) The Board... Board systems are informed of all requirements necessary to protect the privacy of individuals. The...

4 CFR 200.13 - Privacy Act training.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 4 Accounts 1 2013-01-01 2013-01-01 false Privacy Act training. 200.13 Section 200.13 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.13 Privacy Act training. (a) The... any Board systems of records are informed of all requirements necessary to protect the privacy of...

48 CFR 1452.224-1 - Privacy Act Notification.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 48 Federal Acquisition Regulations System 5 2011-10-01 2011-10-01 false Privacy Act Notification... Privacy Act Notification. (a) As prescribed in 1424.104, the clause at FAR 52.224-1, Privacy Act... the clause to read “Privacy Act Notification (JUL 1996) (Deviation)”; and (2) Adding the following...

16 CFR 313.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 16 Commercial Practices 1 2013-01-01 2013-01-01 false Revised privacy notices. 313.8 Section 313.8 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.8 Revised privacy notices. (a) General rule. Except...

4 CFR 200.13 - Privacy Act training.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 4 Accounts 1 2011-01-01 2011-01-01 false Privacy Act training. 200.13 Section 200.13 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.13 Privacy Act training. (a) The... any Board systems of records are informed of all requirements necessary to protect the privacy of...

45 CFR 503.1 - Definitions-Privacy Act.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 3 2011-10-01 2011-10-01 false Definitions-Privacy Act. 503.1 Section 503.1... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.1 Definitions—Privacy Act. For the purpose of this part: Agency...

12 CFR 216.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 2 2011-01-01 2011-01-01 false Revised privacy notices. 216.8 Section 216.8 Banks and Banking FEDERAL RESERVE SYSTEM BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.8 Revised privacy notices...

16 CFR 313.8 - Revised privacy notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 16 Commercial Practices 1 2014-01-01 2014-01-01 false Revised privacy notices. 313.8 Section 313.8 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.8 Revised privacy notices. (a) General rule. Except...

12 CFR 216.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 2 2012-01-01 2012-01-01 false Revised privacy notices. 216.8 Section 216.8 Banks and Banking FEDERAL RESERVE SYSTEM BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.8 Revised privacy notices...

10 CFR 1304.113 - Privacy Act training.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Privacy Act training. 1304.113 Section 1304.113 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.113 Privacy Act training. (a) The Board... Board systems are informed of all requirements necessary to protect the privacy of individuals. The...

12 CFR 716.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 7 2013-01-01 2013-01-01 false Revised privacy notices. 716.8 Section 716.8 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING CREDIT UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.8 Revised privacy notices. (a) General...

16 CFR 313.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 16 Commercial Practices 1 2012-01-01 2012-01-01 false Revised privacy notices. 313.8 Section 313.8 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.8 Revised privacy notices. (a) General rule. Except...

48 CFR 1452.224-1 - Privacy Act Notification.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 48 Federal Acquisition Regulations System 5 2013-10-01 2013-10-01 false Privacy Act Notification... Privacy Act Notification. (a) As prescribed in 1424.104, the clause at FAR 52.224-1, Privacy Act... the clause to read “Privacy Act Notification (JUL 1996) (Deviation)”; and (2) Adding the following...

10 CFR 1304.113 - Privacy Act training.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 4 2013-01-01 2013-01-01 false Privacy Act training. 1304.113 Section 1304.113 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.113 Privacy Act training. (a) The Board... Board systems are informed of all requirements necessary to protect the privacy of individuals. The...

16 CFR 313.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 16 Commercial Practices 1 2011-01-01 2011-01-01 false Revised privacy notices. 313.8 Section 313.8 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.8 Revised privacy notices. (a) General rule. Except...

12 CFR 1016.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 8 2013-01-01 2013-01-01 false Revised privacy notices. 1016.8 Section 1016.8 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 1016.8 Revised privacy notices. (a) General rule. Except as...

4 CFR 200.4 - Privacy Act inquiries.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 4 Accounts 1 2012-01-01 2012-01-01 false Privacy Act inquiries. 200.4 Section 200.4 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.4 Privacy Act inquiries. (a... Avenue, NW., Suite 700, Washington, DC 20006. Inquiries should be marked “Privacy Act Inquiry” on each...

32 CFR 505.12 - Privacy Act enforcement actions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 3 2011-07-01 2009-07-01 true Privacy Act enforcement actions. 505.12 Section... AUTHORITIES AND PUBLIC RELATIONS ARMY PRIVACY ACT PROGRAM § 505.12 Privacy Act enforcement actions. (a.... (1) Civil remedies. The DA is subject to civil remedies for violations of the Privacy Act. In...

12 CFR 716.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 6 2011-01-01 2011-01-01 false Revised privacy notices. 716.8 Section 716.8 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING CREDIT UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.8 Revised privacy notices. (a) General...

12 CFR 216.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 2 2013-01-01 2013-01-01 false Revised privacy notices. 216.8 Section 216.8 Banks and Banking FEDERAL RESERVE SYSTEM BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.8 Revised privacy notices...

4 CFR 200.4 - Privacy Act inquiries.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 4 Accounts 1 2011-01-01 2011-01-01 false Privacy Act inquiries. 200.4 Section 200.4 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.4 Privacy Act inquiries. (a... Avenue, NW., Suite 700, Washington, DC 20006. Inquiries should be marked “Privacy Act Inquiry” on each...

48 CFR 1452.224-1 - Privacy Act Notification.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 48 Federal Acquisition Regulations System 5 2012-10-01 2012-10-01 false Privacy Act Notification... Privacy Act Notification. (a) As prescribed in 1424.104, the clause at FAR 52.224-1, Privacy Act... the clause to read “Privacy Act Notification (JUL 1996) (Deviation)”; and (2) Adding the following...

12 CFR 716.8 - Revised privacy notices.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Revised privacy notices. 716.8 Section 716.8 Banks and Banking NATIONAL CREDIT UNION ADMINISTRATION REGULATIONS AFFECTING CREDIT UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.8 Revised privacy notices. (a) General...

75 FR 81454 - Privacy Act of 1974; Implementation

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-28

... JOINT BOARD FOR ENROLLMENT OF ACTUARIES 20 CFR Part 903 Privacy Act of 1974; Implementation AGENCY... requirements of the Privacy Act of 1974, as amended, the Joint Board for the Enrollment of Actuaries (Joint... Privacy Act, from certain of the Privacy Act's provisions, to revise language that incorrectly implies...

45 CFR 503.1 - Definitions-Privacy Act.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 3 2010-10-01 2010-10-01 false Definitions-Privacy Act. 503.1 Section 503.1... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.1 Definitions—Privacy Act. For the purpose of this part: Agency...

12 CFR 716.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Initial privacy notice to consumers required... CREDIT UNIONS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 716.4 Initial privacy notice to consumers required. (a) Initial notice requirement. You must provide a clear and...

4 CFR 200.4 - Privacy Act inquiries.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 4 Accounts 1 2010-01-01 2010-01-01 false Privacy Act inquiries. 200.4 Section 200.4 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.4 Privacy Act inquiries. (a... Avenue, NW., Suite 700, Washington, DC 20006. Inquiries should be marked “Privacy Act Inquiry” on each...

48 CFR 1452.224-1 - Privacy Act Notification.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Privacy Act Notification... Privacy Act Notification. (a) As prescribed in 1424.104, the clause at FAR 52.224-1, Privacy Act... the clause to read “Privacy Act Notification (JUL 1996) (Deviation)”; and (2) Adding the following...

10 CFR 1304.113 - Privacy Act training.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Privacy Act training. 1304.113 Section 1304.113 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.113 Privacy Act training. (a) The Board... Board systems are informed of all requirements necessary to protect the privacy of individuals. The...

17 CFR 160.8 - Revised privacy notices.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Revised privacy notices. 160.8 Section 160.8 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 160.8 Revised privacy notices. (a) General rule. Except...

12 CFR 216.8 - Revised privacy notices.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 2 2010-01-01 2010-01-01 false Revised privacy notices. 216.8 Section 216.8 Banks and Banking FEDERAL RESERVE SYSTEM BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.8 Revised privacy notices...

16 CFR 313.8 - Revised privacy notices.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 16 Commercial Practices 1 2010-01-01 2010-01-01 false Revised privacy notices. 313.8 Section 313.8 Commercial Practices FEDERAL TRADE COMMISSION REGULATIONS UNDER SPECIFIC ACTS OF CONGRESS PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 313.8 Revised privacy notices. (a) General rule. Except...

4 CFR 200.13 - Privacy Act training.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 4 Accounts 1 2010-01-01 2010-01-01 false Privacy Act training. 200.13 Section 200.13 Accounts RECOVERY ACCOUNTABILITY AND TRANSPARENCY BOARD PRIVACY ACT OF 1974 § 200.13 Privacy Act training. (a) The... any Board systems of records are informed of all requirements necessary to protect the privacy of...

77 FR 57015 - Privacy Act; Implementation

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-17

... DEPARTMENT OF DEFENSE Office of the Secretary [Docket ID DoD-2012-OS-0104] 32 CFR Part 319 Privacy... the records in another Privacy Act system of records. DIA is updating the DIA Privacy Act Program by... its Privacy Programs. DoD expects no opposition to the changes and no significant adverse comments...

77 FR 2721 - Privacy Act System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-19

... FEDERAL COMMUNICATIONS COMMISSION Privacy Act System of Records AGENCY: Federal Communications Commission (FCC, Commission, or Agency). ACTION: Notice; one new Privacy Act system of records. SUMMARY: Pursuant to subsection (e)(4) of the Privacy Act of 1974, as amended (``Privacy Act''), 5 U.S.C. 552a, the...

Perspectives on Privacy and Terrorism: All Is not Lost--Yet.

ERIC Educational Resources Information Center

Gellman, Robert

2002-01-01

Discusses implications of the U.S.A. Patriot Act, antiterrorism legislation that was passed after the September 11 attacks, regarding privacy issues. Highlights include information privacy; privacy and government, including increases in the surveillance powers of government; privacy and the private sector; and future possibilities. (Author/LRW)

75 FR 11191 - Privacy Act of 1974; Retirement of Department of Homeland Security Federal Emergency Management...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-10

... 20472. For privacy issues please contact: Mary Ellen Callahan (703-235- 0780), Chief Privacy Officer... DEPARTMENT OF HOMELAND SECURITY Office of the Secretary Privacy Act of 1974; Retirement of Department of Homeland Security Federal Emergency Management Agency System of Records AGENCY: Privacy Office...

75 FR 36535 - Freedom of Information Act, Privacy Act of 1974; Implementation

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-28

... Freedom of Information Act (FOIA) and its regulations concerning the Privacy Act of 1974 (Privacy Act). It..., Privacy Act of 1974; Implementation AGENCY: Department of the Treasury. ACTION: Final rule; correcting... the Privacy Act. In addition, that document revised the list of Treasury offices and bureaus found...

Employee Privacy Rights: A Management Guide.

ERIC Educational Resources Information Center

Shepard, Ira Michael; Olsen, Harry

Employee privacy rights are considered, along with practical problems and permissible parameters of employer activity. Included is a state-by-state analysis of the status of workplace privacy. Definitions are offered of "invasion of privacy," with attention to four types of privacy invasions: (1) placing someone in a "false light," (2) the public…

Privacy Awareness: A Means to Solve the Privacy Paradox?

NASA Astrophysics Data System (ADS)

Pötzsch, Stefanie

People are limited in their resources, i.e. they have limited memory capabilities, cannot pay attention to too many things at the same time, and forget much information after a while; computers do not suffer from these limitations. Thus, revealing personal data in electronic communication environments and being completely unaware of the impact of privacy might cause a lot of privacy issues later. Even if people are privacy aware in general, the so-called privacy paradox shows that they do not behave according to their stated attitudes. This paper discusses explanations for the existing dichotomy between the intentions of people towards disclosure of personal data and their behaviour. We present requirements on tools for privacy-awareness support in order to counteract the privacy paradox.

Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge.

PubMed

Yu, Fei; Ji, Zhanglong

2014-01-01

In response to the growing interest in genome-wide association study (GWAS) data privacy, the Integrating Data for Analysis, Anonymization and SHaring (iDASH) center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healthcare Privacy Protection Challenge. We apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the challenge's data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the χ2 statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods.

Scalable privacy-preserving data sharing methodology for genome-wide association studies: an application to iDASH healthcare privacy protection challenge

PubMed Central

2014-01-01

In response to the growing interest in genome-wide association study (GWAS) data privacy, the Integrating Data for Analysis, Anonymization and SHaring (iDASH) center organized the iDASH Healthcare Privacy Protection Challenge, with the aim of investigating the effectiveness of applying privacy-preserving methodologies to human genetic data. This paper is based on a submission to the iDASH Healthcare Privacy Protection Challenge. We apply privacy-preserving methods that are adapted from Uhler et al. 2013 and Yu et al. 2014 to the challenge's data and analyze the data utility after the data are perturbed by the privacy-preserving methods. Major contributions of this paper include new interpretation of the χ2 statistic in a GWAS setting and new results about the Hamming distance score, a key component for one of the privacy-preserving methods. PMID:25521367

mSieve: Differential Behavioral Privacy in Time Series of Mobile Sensor Data.

PubMed

Saleheen, Nazir; Chakraborty, Supriyo; Ali, Nasir; Mahbubur Rahman, Md; Hossain, Syed Monowar; Bari, Rummana; Buder, Eugene; Srivastava, Mani; Kumar, Santosh

2016-09-01

Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors.

mSieve: Differential Behavioral Privacy in Time Series of Mobile Sensor Data

PubMed Central

Saleheen, Nazir; Chakraborty, Supriyo; Ali, Nasir; Mahbubur Rahman, Md; Hossain, Syed Monowar; Bari, Rummana; Buder, Eugene; Srivastava, Mani; Kumar, Santosh

2016-01-01

Differential privacy concepts have been successfully used to protect anonymity of individuals in population-scale analysis. Sharing of mobile sensor data, especially physiological data, raise different privacy challenges, that of protecting private behaviors that can be revealed from time series of sensor data. Existing privacy mechanisms rely on noise addition and data perturbation. But the accuracy requirement on inferences drawn from physiological data, together with well-established limits within which these data values occur, render traditional privacy mechanisms inapplicable. In this work, we define a new behavioral privacy metric based on differential privacy and propose a novel data substitution mechanism to protect behavioral privacy. We evaluate the efficacy of our scheme using 660 hours of ECG, respiration, and activity data collected from 43 participants and demonstrate that it is possible to retain meaningful utility, in terms of inference accuracy (90%), while simultaneously preserving the privacy of sensitive behaviors. PMID:28058408

Will the new Australian health privacy law provide adequate protection?

PubMed

Bomba, David; Hallit, George

2002-01-01

Amendments to the original Privacy Act (1988) come at a key point in time, as a national medical record system looms on the Australian horizon. Changes to The Privacy Act have the potential to define a level of information privacy prior to the implementation of such a system. We have therefore collected expert opinions on the ability of the Health Privacy Guidelines (enacted in December 2001 under The Privacy Act and hereafter more specifically known as Health Privacy Legislation) to ensure the privacy and security of patient information. We conclude that the legislation is flawed in its capacity to withstand an increasingly corporatised health sector. Deficiencies in consent requirements, together with feeble enforcement capabilities, mean The Legislation cannot effectively ensure that personally identifiable information will not end up in corporate third party hands. To significantly bolster the new legislation, we argue that it should be supplemented with explicit health data legislation and privacy auditing.

Quantifying Differential Privacy under Temporal Correlations.

PubMed

Cao, Yang; Yoshikawa, Masatoshi; Xiao, Yonghui; Xiong, Li

2017-04-01

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations in the context of continuous data release. First, we model the temporal correlations using Markov model and analyze the privacy leakage of a DP mechanism when adversaries have knowledge of such temporal correlations. Our analysis reveals that the privacy loss of a DP mechanism may accumulate and increase over time . We call it temporal privacy leakage . Second, to measure such privacy loss, we design an efficient algorithm for calculating it in polynomial time. Although the temporal privacy leakage may increase over time, we also show that its supremum may exist in some cases. Third, to bound the privacy loss, we propose mechanisms that convert any existing DP mechanism into one against temporal privacy leakage. Experiments with synthetic data confirm that our approach is efficient and effective.

Deriving a Set of Privacy Specific Heuristics for the Assessment of PHRs (Personal Health Records).

PubMed

Furano, Riccardo F; Kushniruk, Andre; Barnett, Jeff

2017-01-01

With the emergence of personal health record (PHR) platforms becoming more widely available, this research focused on the development of privacy heuristics to assess PHRs regarding privacy. Existing sets of heuristics are typically not application specific and do not address patient-centric privacy as a main concern prior to undergoing PHR procurement. A set of privacy specific heuristics were developed based on a scoping review of the literature. An internet-based commercially available, vendor specific PHR application was evaluated using the derived set of privacy specific heuristics. The proposed set of privacy specific derived heuristics is explored in detail in relation to ISO 29100. The assessment of the internet-based commercially available, vendor specific PHR application indicated numerous violations. These violations were noted within the study. It is argued that the new derived privacy heuristics should be used in addition to Nielsen's well-established set of heuristics. Privacy specific heuristics could be used to assess PHR portal system-level privacy mechanisms in the procurement process of a PHR application and may prove to be a beneficial form of assessment to prevent the selection of a PHR platform with a poor privacy specific interface design.

One Size Doesn’t Fit All: Measuring Individual Privacy in Aggregate Genomic Data

PubMed Central

Simmons, Sean; Berger, Bonnie

2017-01-01

Even in the aggregate, genomic data can reveal sensitive information about individuals. We present a new model-based measure, PrivMAF, that provides provable privacy guarantees for aggregate data (namely minor allele frequencies) obtained from genomic studies. Unlike many previous measures that have been designed to measure the total privacy lost by all participants in a study, PrivMAF gives an individual privacy measure for each participant in the study, not just an average measure. These individual measures can then be combined to measure the worst case privacy loss in the study. Our measure also allows us to quantify the privacy gains achieved by perturbing the data, either by adding noise or binning. Our findings demonstrate that both perturbation approaches offer significant privacy gains. Moreover, we see that these privacy gains can be achieved while minimizing perturbation (and thus maximizing the utility) relative to stricter notions of privacy, such as differential privacy. We test PrivMAF using genotype data from the Wellcome Trust Case Control Consortium, providing a more nuanced understanding of the privacy risks involved in an actual genome-wide association studies. Interestingly, our analysis demonstrates that the privacy implications of releasing MAFs from a study can differ greatly from individual to individual. An implementation of our method is available at http://privmaf.csail.mit.edu. PMID:29202050

Privacy in Social Networks: A Survey

NASA Astrophysics Data System (ADS)

Zheleva, Elena; Getoor, Lise

In this chapter, we survey the literature on privacy in social networks. We focus both on online social networks and online affiliation networks. We formally define the possible privacy breaches and describe the privacy attacks that have been studied. We present definitions of privacy in the context of anonymization together with existing anonymization techniques.

Fourteen Reasons Privacy Matters: A Multidisciplinary Review of Scholarly Literature

ERIC Educational Resources Information Center

Magi, Trina J.

2011-01-01

Librarians have long recognized the importance of privacy to intellectual freedom. As digital technology and its applications advance, however, efforts to protect privacy may become increasingly difficult. With some users behaving in ways that suggest they do not care about privacy and with powerful voices claiming that privacy is dead, librarians…

12 CFR 1204.2 - What do the terms in this part mean?

Code of Federal Regulations, 2014 CFR

2014-01-01

... who has primary responsibility for privacy and data protection policy and is authorized to process... Section 1204.2 Banks and Banking FEDERAL HOUSING FINANCE AGENCY ORGANIZATION AND OPERATIONS PRIVACY ACT..., or control. Privacy Act means the Privacy Act of 1974, as amended (5 U.S.C. 552a). Privacy Act...

32 CFR 806b.51 - Privacy and the Web.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 32 National Defense 6 2013-07-01 2013-07-01 false Privacy and the Web. 806b.51 Section 806b.51 National Defense Department of Defense (Continued) DEPARTMENT OF THE AIR FORCE ADMINISTRATION PRIVACY ACT... security notices at major web site entry points and Privacy Act statements or Privacy Advisories when...

17 CFR 248.2 - Model privacy form: rule of construction.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 17 Commodity and Securities Exchanges 3 2010-04-01 2010-04-01 false Model privacy form: rule of... Safeguarding Personal Information § 248.2 Model privacy form: rule of construction. (a) Model privacy form. Use of the model privacy form in Appendix A to Subpart A of this part, consistent with the instructions...

12 CFR 573.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 6 2014-01-01 2012-01-01 true Model privacy form and examples. 573.2 Section 573.2 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 573.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

17 CFR 160.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 2 2014-04-01 2014-04-01 false Annual privacy notice to... COMMISSION (CONTINUED) PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.5 Annual privacy notice to customers required. (a)(1) General rule. You...

12 CFR 1016.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 8 2014-01-01 2014-01-01 false Delivering privacy and opt out notices. 1016.9 Section 1016.9 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 1016.9 Delivering privacy and opt out notices. (a...

28 CFR 513.50 - Privacy Act requests by inmates.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 28 Judicial Administration 2 2012-07-01 2012-07-01 false Privacy Act requests by inmates. 513.50... ADMINISTRATION ACCESS TO RECORDS Release of Information Privacy Act Requests for Information § 513.50 Privacy Act requests by inmates. Because inmate records are exempt from disclosure under the Privacy Act (see 28 CFR 16...

17 CFR 160.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2012 CFR

2012-04-01

... privacy notices. 160.6 Section 160.6 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.6 Information to be included in privacy notices. (a) General rule. The initial...

12 CFR 216.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 2 2011-01-01 2011-01-01 false Initial privacy notice to consumers required... SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.4 Initial privacy notice to consumers required. (a) Initial notice requirement. You must provide a clear and...

12 CFR 40.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Model privacy form and examples. 40.2 Section 40.2 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 40.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

12 CFR 573.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 6 2013-01-01 2012-01-01 true Revised privacy notices. 573.8 Section 573.8 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 573.8 Revised privacy notices. (a) General rule. Except as otherwise...

12 CFR 573.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 6 2014-01-01 2012-01-01 true Annual privacy notice to customers required. 573.5 Section 573.5 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 573.5 Annual privacy notice to...

17 CFR 160.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 1 2012-04-01 2012-04-01 false Annual privacy notice to... COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.5 Annual privacy notice to customers required. (a)(1) General rule. You must...

17 CFR 248.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 3 2012-04-01 2012-04-01 false Revised privacy notices. 248.8...) REGULATIONS S-P AND S-AM Regulation S-P: Privacy of Consumer Financial Information and Safeguarding Personal Information Privacy and Opt Out Notices § 248.8 Revised privacy notices. (a) General rule. Except as otherwise...

28 CFR 513.50 - Privacy Act requests by inmates.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 2 2013-07-01 2013-07-01 false Privacy Act requests by inmates. 513.50... ADMINISTRATION ACCESS TO RECORDS Release of Information Privacy Act Requests for Information § 513.50 Privacy Act requests by inmates. Because inmate records are exempt from disclosure under the Privacy Act (see 28 CFR 16...

32 CFR 701.118 - Privacy, IT, and PIAs.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 32 National Defense 5 2012-07-01 2012-07-01 false Privacy, IT, and PIAs. 701.118 Section 701.118... THE NAVY DOCUMENTS AFFECTING THE PUBLIC DON Privacy Program § 701.118 Privacy, IT, and PIAs. (a) Development. Privacy must be considered when requirements are being analyzed and decisions are being made...

12 CFR 216.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 2 2012-01-01 2012-01-01 false Initial privacy notice to consumers required... SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.4 Initial privacy notice to consumers required. (a) Initial notice requirement. You must provide a clear and...

10 CFR 1304.103 - Privacy Act inquiries.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 4 2012-01-01 2012-01-01 false Privacy Act inquiries. 1304.103 Section 1304.103 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.103 Privacy Act inquiries. (a) Requests... Clarendon Blvd., Suite 1300; Arlington, VA. Requests should be marked “Privacy Act Request” on each page of...

12 CFR 40.8 - Revised privacy notices.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Revised privacy notices. 40.8 Section 40.8 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 40.8 Revised privacy notices. (a) General rule. Except as otherwise...

12 CFR 573.9 - Delivering privacy and opt out notices.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 6 2014-01-01 2012-01-01 true Delivering privacy and opt out notices. 573.9 Section 573.9 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 573.9 Delivering privacy and opt out notices...

10 CFR 1304.103 - Privacy Act inquiries.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 4 2011-01-01 2011-01-01 false Privacy Act inquiries. 1304.103 Section 1304.103 Energy NUCLEAR WASTE TECHNICAL REVIEW BOARD PRIVACY ACT OF 1974 § 1304.103 Privacy Act inquiries. (a) Requests... Clarendon Blvd., Suite 1300; Arlington, VA. Requests should be marked “Privacy Act Request” on each page of...

45 CFR 503.2 - General policies-Privacy Act.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 45 Public Welfare 3 2012-10-01 2012-10-01 false General policies-Privacy Act. 503.2 Section 503.2... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.2 General policies—Privacy Act. The Commission will protect the...

17 CFR 160.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 2 2014-04-01 2014-04-01 false Initial privacy notice to... COMMISSION (CONTINUED) PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.4 Initial privacy notice to consumers required. (a) Initial notice...

17 CFR 248.8 - Revised privacy notices.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 17 Commodity and Securities Exchanges 4 2014-04-01 2014-04-01 false Revised privacy notices. 248.8...) REGULATIONS S-P, S-AM, AND S-ID Regulation S-P: Privacy of Consumer Financial Information and Safeguarding Personal Information Privacy and Opt Out Notices § 248.8 Revised privacy notices. (a) General rule. Except...

12 CFR 573.2 - Model privacy form and examples.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 5 2011-01-01 2011-01-01 false Model privacy form and examples. 573.2 Section 573.2 Banks and Banking OFFICE OF THRIFT SUPERVISION, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 573.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

17 CFR 160.6 - Information to be included in privacy notices.

Code of Federal Regulations, 2014 CFR

2014-04-01

... privacy notices. 160.6 Section 160.6 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION (CONTINUED) PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.6 Information to be included in privacy notices. (a) General rule. The...

45 CFR 503.2 - General policies-Privacy Act.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 45 Public Welfare 3 2013-10-01 2013-10-01 false General policies-Privacy Act. 503.2 Section 503.2... THE UNITED STATES, DEPARTMENT OF JUSTICE RULES OF PRACTICE PRIVACY ACT AND GOVERNMENT IN THE SUNSHINE REGULATIONS Privacy Act Regulations § 503.2 General policies—Privacy Act. The Commission will protect the...

12 CFR 40.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 1 2013-01-01 2013-01-01 false Revised privacy notices. 40.8 Section 40.8 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 40.8 Revised privacy notices. (a) General rule. Except as otherwise...

12 CFR 40.8 - Revised privacy notices.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 1 2012-01-01 2012-01-01 false Revised privacy notices. 40.8 Section 40.8 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 40.8 Revised privacy notices. (a) General rule. Except as otherwise...

12 CFR 332.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 5 2013-01-01 2013-01-01 false Annual privacy notice to customers required... OF GENERAL POLICY PRIVACY OF CONSUMER FINANCIAL INFORMATION Privacy and Opt Out Notices § 332.5 Annual privacy notice to customers required. (a)(1) General rule. You must provide a clear and...

12 CFR 40.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 1 2014-01-01 2014-01-01 false Model privacy form and examples. 40.2 Section 40.2 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 40.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

12 CFR 216.5 - Annual privacy notice to customers required.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 2 2011-01-01 2011-01-01 false Annual privacy notice to customers required... SYSTEM PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) Privacy and Opt Out Notices § 216.5 Annual privacy notice to customers required. (a)(1) General rule. You must provide a clear and...

12 CFR 1016.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 8 2013-01-01 2013-01-01 false Model privacy form and examples. 1016.2 Section 1016.2 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 1016.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

28 CFR 513.50 - Privacy Act requests by inmates.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 28 Judicial Administration 2 2014-07-01 2014-07-01 false Privacy Act requests by inmates. 513.50... ADMINISTRATION ACCESS TO RECORDS Release of Information Privacy Act Requests for Information § 513.50 Privacy Act requests by inmates. Because inmate records are exempt from disclosure under the Privacy Act (see 28 CFR 16...

17 CFR 248.8 - Revised privacy notices.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 17 Commodity and Securities Exchanges 3 2013-04-01 2013-04-01 false Revised privacy notices. 248.8...) REGULATIONS S-P AND S-AM Regulation S-P: Privacy of Consumer Financial Information and Safeguarding Personal Information Privacy and Opt Out Notices § 248.8 Revised privacy notices. (a) General rule. Except as otherwise...

12 CFR 40.2 - Model privacy form and examples.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 12 Banks and Banking 1 2013-01-01 2013-01-01 false Model privacy form and examples. 40.2 Section 40.2 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY PRIVACY OF CONSUMER FINANCIAL INFORMATION § 40.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

25 CFR 700.259 - Records subject to Privacy Act.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 25 Indians 2 2013-04-01 2013-04-01 false Records subject to Privacy Act. 700.259 Section 700.259 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES Privacy Act § 700.259 Records subject to Privacy Act. The Privacy Act applies to all “records” as that...

17 CFR 160.4 - Initial privacy notice to consumers required.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 1 2012-04-01 2012-04-01 false Initial privacy notice to... COMMISSION PRIVACY OF CONSUMER FINANCIAL INFORMATION UNDER TITLE V OF THE GRAMM-LEACH-BLILEY ACT Privacy and Opt Out Notices § 160.4 Initial privacy notice to consumers required. (a) Initial notice requirement...

25 CFR 700.259 - Records subject to Privacy Act.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 25 Indians 2 2012-04-01 2012-04-01 false Records subject to Privacy Act. 700.259 Section 700.259 Indians THE OFFICE OF NAVAJO AND HOPI INDIAN RELOCATION COMMISSION OPERATIONS AND RELOCATION PROCEDURES Privacy Act § 700.259 Records subject to Privacy Act. The Privacy Act applies to all “records” as that...

12 CFR 1016.2 - Model privacy form and examples.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 8 2014-01-01 2014-01-01 false Model privacy form and examples. 1016.2 Section 1016.2 Banks and Banking BUREAU OF CONSUMER FINANCIAL PROTECTION PRIVACY OF CONSUMER FINANCIAL INFORMATION (REGULATION P) § 1016.2 Model privacy form and examples. (a) Model privacy form. Use of the model...

28 CFR 513.50 - Privacy Act requests by inmates.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 28 Judicial Administration 2 2011-07-01 2011-07-01 false Privacy Act requests by inmates. 513.50... ADMINISTRATION ACCESS TO RECORDS Release of Information Privacy Act Requests for Information § 513.50 Privacy Act requests by inmates. Because inmate records are exempt from disclosure under the Privacy Act (see 28 CFR 16...